A Survey on Multi-View Clustering

Restarted multiple kernel algorithms with self-guiding for large-scale multi-view clustering

Multi-view clustering is a powerful approach for discovering underlying structures hidden behind diverse views of datasets. Most existing multi-view spectral clustering methods use fixed similarity matrices or alternately updated ones. However, the former often fall short in adaptively capturing relationships among different views, while the latter are often time-consuming and even impractical for large-scale datasets. To the best of our knowledge, there are no multi-view spectral clustering methods can both construct multi-view similarity matrices inexpensively and preserve the valuable clustering insights from previous cycles at the same time. To fill in this gap, we present a Sum-Ratio Multi-view Ncut model that share a common representation embedding for multi-view data. Based on this model, we propose a restarted multi-view multiple kernel clustering framework with self-guiding. To release the overhead, we use similarity matrices with strict block diagonal representation, and present an efficient multiple kernel selection technique. Comprehensive experiments on benchmark multi-view datasets demonstrate that, even using randomly generated initial guesses, the restarted algorithms can improve the clustering performances by 5-10 times for some popular multi-view clustering methods. Specifically, our framework offers a potential boosting effect for most of the state-of-the-art multi-view clustering algorithms at very little cost, especially for those with poor performances.

Anchors Crash Tensor: Efficient and Scalable Tensorial Multi-View Subspace Clustering

Tensorial Multi-view Clustering (TMC), a prominent approach in multi-view clustering, leverages low-rank tensor learning to capture high-order correlation among views for consistent clustering structure identification. Despite its promising performance, the TMC algorithms face three key challenges: 1). The severe computational burden makes it difficult for TMC methods to handle large-scale datasets. 2). Estimation bias problem caused by the convex surrogate of the tensor rank. 3). Lack of explicit balance of consistency and complementarity. Being aware of these, we propose a basic framework Efficient and Scalable Tensorial Multi-View Subspace Clustering (ESTMC) for large-scale multi-view clustering. ESTMC integrates anchor representation learning and non-convex function-based low-rank tensor learning with a Generalized Non-convex Tensor Rank (GNTR) into a unified objective function, which enhances the efficiency of the existing subspace-based TMC framework. Furthermore, a novel model ESTMC-C with the proposed Enhanced Tensor Rank (ETR), Consistent Geometric Regularization (CGR), and Tensorial Exclusive Regularization (TER) is extended to balance the learning of consistency and complementarity among views, delivering divisible representations for the clustering task. Efficient iterative optimization algorithms are designed to solve the proposed ESTMC and ESTMC-C, which enjoy time-economical complexity and exhibit theoretical convergence. Extensive experimental results on various datasets demonstrate the superiority of the proposed algorithms as compared to state-of-the-art methods.

Cross-View Approximation on Grassmann Manifold for Multiview Clustering

In existing multiview clustering research, the comprehensive learning from multiview graph and feature spaces simultaneously remains insufficient when achieving a consistent clustering structure. In addition, a postprocessing step is often required. In light of these considerations, a cross-view approximation on Grassman manifold (CAGM) model is proposed to address inconsistencies within multiview adjacency matrices, feature matrices, and cross-view combinations from the two sources. The model uses a ratio-formed objective function, enabling parameter-free bidirectional fusion. Furthermore, the CAGM model incorporates a paired encoding mechanism to generate low-dimensional and orthogonal cross-view embeddings. Through the approximation of two measurable subspaces on the Grassmann manifold, the direct acquisition of the indicator matrix is realized. Furthermore, an effective optimization algorithm corresponding to the CAGM model is derived. Comprehensive experiments on four real-world datasets are conducted to substantiate the effectiveness of our proposed method.

Contrastive Multiview Attribute Graph Clustering With Adaptive Encoders

Multiview attribute graph clustering aims to cluster nodes into disjoint categories by taking advantage of the multiview topological structures and the node attribute values. However, the existing works fail to explicitly discover the inherent relationships in multiview topological graph matrices while considering different properties between the graphs. Besides, they cannot well handle the sparse structure of some graphs in the learning procedure of graph embeddings. Therefore, in this article, we propose a novel contrastive multiview attribute graph clustering (CMAGC) with adaptive encoders method. Within this framework, the adaptive encoders concerning different properties of distinct topological graphs are chosen to integrate multiview attribute graph information by checking whether there exists high-order neighbor information or not. Meanwhile, the number of layers of the GCN encoders is selected according to the prior knowledge related to the characteristics of different topological graphs. In particular, the feature-level and cluster-level contrastive learning are conducted on the multiview soft assignment representations, where the union of the first-order neighbors from the corresponding graph pairs is regarded as the positive pairs for data augmentation and the sparse neighbor information problem in some graphs can be well dealt with. To the best of our knowledge, it is the first time to explicitly deal with the inherent relationships from the interview and intraview perspectives. Extensive experiments are conducted on several datasets to verify the superiority of the proposed CMAGC method compared with the state-of-the-art methods.

Anchor-Sharing and Cluster-Wise Contrastive Network for Multiview Representation Learning

Multiview clustering (MVC) has gained significant attention as it enables the partitioning of samples into their respective categories through unsupervised learning. However, there are a few issues as follows: 1) many existing deep clustering methods use the same latent features to achieve the conflict objectives, namely, reconstruction and view consistency. The reconstruction objective aims to preserve view-specific features for each individual view, while the view-consistency objective strives to obtain common features across all views; 2) some deep embedded clustering (DEC) approaches adopt view-wise fusion to obtain consensus feature representation. However, these approaches overlook the correlation between samples, making it challenging to derive discriminative consensus representations; and 3) many methods use contrastive learning (CL) to align the view's representations; however, they do not take into account cluster information during the construction of sample pairs, which can lead to the presence of false negative pairs. To address these issues, we propose a novel multiview representation learning network, called anchor-sharing and clusterwise CL (CwCL) network for multiview representation learning. Specifically, we separate view-specific learning and view-common learning into different network branches, which addresses the conflict between reconstruction and consistency. Second, we design an anchor-sharing feature aggregation (ASFA) module, which learns the sharing anchors from different batch data samples, establishes the bipartite relationship between anchors and samples, and further leverages it to improve the samples' representations. This module enhances the discriminative power of the common representation from different samples. Third, we design CwCL module, which incorporates the learned transition probability into CL, allowing us to focus on minimizing the similarity between representations from negative pairs with a low transition probability. It alleviates the conflict in previous sample-level contrastive alignment. Experimental results demonstrate that our method outperforms the state-of-the-art performance.

Partition-level fusion induced multi-view Subspace Clustering with Tensorial Geman Rank

The tensor-based multi-view clustering approach captures the high-order correlation among different views by learning a low-rank representation tensor, which has achieved favorable performance in multi-view clustering. However, the tensor rank approximation functions used by the extant algorithms are not tight enough to the true rank of the tensor, leading to the undesired low-rank structure. Besides, the fusion strategy at the affinity matrix level is less robust to noise, resulting in sub-optimal clustering results. To tackle these issues, we propose a Partition-Level Fusion Induced Multi-view Subspace Clustering with Tensorial Geman Rank (PFMSC-TGR). Firstly, a tighter surrogate of tensor rank is designed, named Tensorial Geman Rank (TGR). Under the constraint of TGR, all non-zero singular values are penalized with suitable strength, leading to a strongly discriminative representation tensor. Secondly, we fuse the information of all views at the partition level to obtain a consistent indicator matrix, which enhances the stability of the model against noisy information. Furthermore, we combine these two items in a unified framework and employ an efficient algorithm to optimize the objective function. We further mathematically prove that the sequences constructed by our proposed algorithm converge to the stationary KKT point. Extensive experiments are conducted on nine data sets with different types and sizes, and the results of comparison with the eleven state-of-the-art algorithms prove the superiority of our algorithm. Our code is publicly available at: https://github.com/jijintian/PFMSC-TGR.

Model-based multifacet clustering with high-dimensional omics applications

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

Progressive Neighbor-masked Contrastive Learning for Fusion-style Deep Multi-view Clustering

Fusion-style Deep Multi-view Clustering (FDMC) can efficiently integrate comprehensive feature information from latent embeddings of multiple views and has drawn much attention recently. However, existing FDMC methods suffer from the interference of view-specific information for fusion representation, affecting the learning of discriminative cluster structure. In this paper, we propose a new framework of Progressive Neighbor-masked Contrastive Learning for FDMC (PNCL-FDMC) to tackle the aforementioned issues. Specifically, by using neighbor-masked contrastive learning, PNCL-FDMC can explicitly maintain the local structure during the embedding aggregation, which is beneficial to the common semantics enhancement on the fusion view. Based on the consistent aggregation, the fusion view is further enhanced by diversity-aware cluster structure enhancement. In this process, the enhanced cluster assignments and cluster discrepancies are employed to guide the weighted neighbor-masked contrastive alignment of semantic structure between individual views and the fusion view. Extensive experiments validate the effectiveness of the proposed framework, revealing its ability in discriminative representation learning and improving clustering performance.

A coarse and fine-grained deep multi view subspace clustering method for unsupervised fault diagnosis of rolling bearings

To address the issue of insufficient characterization of fault features in inherent vibration data that affects the performance of unsupervised learning-based fault diagnosis, a coarse and fine-grained deep multi view subspace clustering method (CFG-DMVSC) for unsupervised fault diagnosis of rolling bearings is proposed. The proposed method designs a convolutional autoencoder network based on the Gramian angular field transformation for multi-signal analysis domains. A multi-view coarse-grained self-expressive method based on information entropy is designed to handle differences in information across different views. Furthermore, a fine-grained common and independent information separation loss function based on mutual information is proposed to ensure compactness among multiple views. Both the Case Western Reserve University rolling bearing dataset and privately built bearing fault test bench data demonstrate that, compared to existing methods, the proposed method can perform coarse and fine-grained division in multi-view subspaces, achieving better clustering diagnosis performance on the extracted common information among views.

One-Step Multiview Fuzzy Clustering With Collaborative Learning Between Common and Specific Hidden Space Information

Multiview data are widespread in real-world applications, and multiview clustering is a commonly used technique to effectively mine the data. Most of the existing algorithms perform multiview clustering by mining the commonly hidden space between views. Although this strategy is effective, there are two challenges that still need to be addressed to further improve the performance. First, how to design an efficient hidden space learning method so that the learned hidden spaces contain both shared and specific information of multiview data. Second, how to design an efficient mechanism to make the learned hidden space more suitable for the clustering task. In this study, a novel one-step multiview fuzzy clustering (OMFC-CS) method is proposed to address the two challenges by collaborative learning between the common and specific space information. To tackle the first challenge, we propose a mechanism to extract the common and specific information simultaneously based on matrix factorization. For the second challenge, we design a one-step learning framework to integrate the learning of common and specific spaces and the learning of fuzzy partitions. The integration is achieved in the framework by performing the two learning processes alternately and thereby yielding mutual benefit. Furthermore, the Shannon entropy strategy is introduced to obtain the optimal views weight assignment during clustering. The experimental results based on benchmark multiview datasets demonstrate that the proposed OMFC-CS outperforms many existing methods.

Clustering scRNA-seq data with the cross-view collaborative information fusion strategy

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.

Fast Multiview Clustering by Optimal Graph Mining

Multiview clustering (MVC) aims to exploit heterogeneous information from different sources and was extensively investigated in the past decade. However, far less attention has been paid to handling large-scale multiview data. In this brief, we fill this gap and propose a fast multiview clustering by an optimal graph mining model to handle large-scale data. We mine a consistent clustering structure from landmark-based graphs of different views, from which the optimal graph based on the one-hot encoding of cluster labels is recovered. Our model is parameter-free, so intractable hyperparameter tuning is avoided. An efficient algorithm of linear complexity to the number of samples is developed to solve the optimization problems. Extensive experiments on real-world datasets of various scales demonstrate the superiority of our proposal.

Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering

In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP.

Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k -means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via u nified and d iscrete b ipartite g raph l earning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

Multiview Clustering With Propagating Information Bottleneck

In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources. Observing the rich closely related multivariate (i.e., source and view) information and the potential dynamic information flow interacting among them, in this article, a general hierarchical information propagation model is first built to address the above challenging problem. It describes the process from optimal feature subspace learning (OFSL) of each source to final clustering structure learning (CSL). Then, a novel self-guided method named propagating information bottleneck (PIB) is proposed to realize the model. It works in a circulating propagation fashion, so that the resulting clustering structure obtained from the last iteration can "self-guide" the OFSL of each source, and the learned subspaces are in turn used to conduct the subsequent CSL. We theoretically analyze the relationship between the cluster structures learned in the CSL phase and the preservation of relevant information propagated from the OFSL phase. Finally, a two-step alternating optimization method is carefully designed for optimization. Experimental results on various datasets show the superiority of the proposed PIB method over several state-of-the-art methods.

Cloud migration framework clustering method for social decision support in modernizing the legacy system

Cloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K‐means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge‐based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra‐cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system.

Deep graph reconstruction for multi-view clustering

Graph-based multi-view clustering methods have achieved impressive success by exploring a complemental or independent graph embedding with low-dimension among multiple views. The majority of them, however, are shallow models with limited ability to learn the nonlinear information in multi-view data. To this end, we propose a novel deep graph reconstruction (DGR) framework for multi-view clustering, which contains three modules. Specifically, a Multi-graph Fusion Module (MFM) is employed to obtain the consensus graph. Then node representation is learned by the Graph Embedding Network (GEN). To assign clusters directly, the Clustering Assignment Module (CAM) is devised to obtain the final low-dimensional graph embedding, which can serve as the indicator matrix. In addition, a simple and powerful loss function is designed in the proposed DGR. Extensive experiments on seven real-world datasets have been conducted to verify the superior clustering performance and efficiency of DGR compared with the state-of-the-art methods.

An overview of clustering methods with guidelines for application in mental health research

Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.

Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach

Kernel k-means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Its merits are thoroughly validated on a suite of simulated datasets and real data benchmarks that feature nonlinear and multi-view separation. Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from the non-convexity of the underlying objective function. In this paper, we generalize recent results leveraging a general family of means to combat sub-optimal local solutions to the kernel and multi-kernel settings. Called Kernel Power k-Means, our algorithm uses majorization-minimization (MM) to better solve this non-convex problem. We show that the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates. We rigorously characterize its convergence properties both from computational and statistical points of view. In particular, we characterize the large sample behavior of the proposed method by establishing strong consistency guarantees as well as finite-sample bounds on the excess risk of the estimates through modern tools in learning theory. The proposal's efficacy is demonstrated through an array of simulated and real data experiments.

How our understanding of memory replay evolves

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes

The identification of cancer subtypes can help researchers understand hidden genomic mechanisms, enhance diagnostic accuracy and improve clinical treatments. With the development of high-throughput techniques, researchers can access large amounts of data from multiple sources. Because of the high dimensionality and complexity of multiomics and clinical data, research into the integration of multiomics data is needed, and developing effective tools for such purposes remains a challenge for researchers. In this work, we proposed an entirely unsupervised clustering method without harnessing any prior knowledge (MODEC). We used manifold optimization and deep-learning techniques to integrate multiomics data for the identification of cancer subtypes and the analysis of significant clinical variables. Since there is nonlinearity in the gene-level datasets, we used manifold optimization methodology to extract essential information from the original omics data to obtain a low-dimensional latent subspace. Then, MODEC uses a deep learning-based clustering module to iteratively define cluster centroids and assign cluster labels to each sample by minimizing the Kullback-Leibler divergence loss. MODEC was applied to six public cancer datasets from The Cancer Genome Atlas database and outperformed eight competing methods in terms of the accuracy and reliability of the subtyping results. MODEC was extremely competitive in the identification of survival patterns and significant clinical features, which could help doctors monitor disease progression and provide more suitable treatment strategies.

Fusing Local and Global Information for One-Step Multi-View Subspace Clustering

Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information

In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.