Multi-View Data Fusion Oriented Clustering via Nuclear Norm Minimization

Deep Multiview Adaptive Clustering With Semantic Invariance

Multiview clustering has attracted significant attention in various fields, due to the superiority in mining patterns of multiview data. However, previous methods are still confronted with two challenges. First, they do not fully consider the semantic invariance of multiview data in aggregating complementary information, degrading semantic robustness of fusion representations. Second, they rely on predefined clustering strategies to mine patterns, lacking adequate explorations of data structures. To address the challenges, deep multiview adaptive clustering via semantic invariance (DMAC-SI) is proposed, which learns an adaptive clustering strategy on semantics-robust fusion representations to fully explore structures in mining patterns. Specifically, a mirror fusion architecture is devised to explore interview invariance and intrainstance invariance hidden in multiview data, which captures invariant semantics of complementary information to learn semantics-robust fusion representations. Then, a Markov decision process of multiview data partitions is proposed within the reinforcement learning framework, which learns an adaptive clustering strategy on semantics-robust fusion representations to guarantee the structure explorations in mining patterns. The two components seamlessly collaborate in an end-to-end manner to accurately partition multiview data. Finally, extensive experiment results on five benchmark datasets demonstrate that DMAC-SI outperforms the state-of-the-art methods.

Fast Continual Multi-View Clustering With Incomplete Views

Multi-view clustering (MVC) has attracted broad attention due to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). Specifically, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works have proposed ways to handle this problem, but all of them fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is difficult to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address this issue. Specifically, the method maintains a scalable consensus coefficient matrix and updates its knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the given views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. In addition, we design a three-step iterative algorithm to solve the resultant problem with linear complexity and proven convergence. Comprehensive experiments conducted on various datasets demonstrate the superiority of FCMVC-IV over the competing approaches. The code is publicly available at https://github.com/wanxinhang/FCMVC-IV.

Multiview Clustering of Adaptive Sparse Representation Based on Coupled P Systems

A multiview clustering (MVC) has been a significant technique to dispose data mining issues. Most of the existing studies on this topic adopt a fixed number of neighbors when constructing the similarity matrix of each view, like single-view clustering. However, this may reduce the clustering effect due to the diversity of multiview data sources. Moreover, most MVC utilizes iterative optimization to obtain clustering results, which consumes a significant amount of time. Therefore, this paper proposes a multiview clustering of adaptive sparse representation based on coupled P system (MVCS-CP) without iteration. The whole algorithm flow runs in the coupled P system. Firstly, the natural neighbor search algorithm without parameters automatically determines the number of neighbors of each view. In turn, manifold learning and sparse representation are employed to construct the similarity matrix, which preserves the internal geometry of the views. Next, a soft thresholding operator is introduced to form the unified graph to gain the clustering results. The experimental results on nine real datasets indicate that the MVCS-CP outperforms other state-of-the-art comparison algorithms.

High-Order Correlation Preserved Incomplete Multi-View Subspace Clustering

Incomplete multi-view clustering aims to exploit the information of multiple incomplete views to partition data into their clusters. Existing methods only utilize the pair-wise sample correlation and pair-wise view correlation to improve the clustering performance but neglect the high-order correlation of samples and that of views. To address this issue, we propose a high-order correlation preserved incomplete multi-view subspace clustering (HCP-IMSC) method which effectively recovers the missing views of samples and the subspace structure of incomplete multi-view data. Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a third-order low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation. Then, a unified affinity matrix can be obtained by fusing the view-specific affinity matrices in a self-weighted manner. A hypergraph is further constructed from the unified affinity matrix to preserve the high-order geometrical structure of the data with incomplete views. Then, the samples with missing views are restricted to be reconstructed by their neighbor samples under the hypergraph-induced hyper-Laplacian regularization. Furthermore, the learning of view-specific affinity matrices as well as the unified one, tensor factorization, and hyper-Laplacian regularization are integrated into a unified optimization framework. An iterative algorithm is designed to solve the resultant model. Experimental results on various benchmark datasets indicate the superiority of the proposed method. The code is implemented by using MATLAB R2018a and MindSpore library: https://github.com/ChangTang/HCP-IMSC.

Embedding Regularizer Learning for Multi-View Semi-Supervised Classification

Classification remains challenging when confronted with the existence of multi-view data with limited labels. In this paper, we propose an embedding regularizer learning scheme for multi-view semi-supervised classification (ERL-MVSC). The proposed framework integrates diversity, sparsity and consensus to dexterously manipulate multi-view data with limited labels. To encourage diversity, ERL-MVSC recasts a linear regression model to derive view-specific embedding regularizers and automatically determines their weights. This is able to tactfully incorporate complementary information of different views. To ensure sparsity, ERL-MVSC imposes l_2,1 -norm on a fused embedding regularizer to exploit the sparse local structure of samples, thereby conveying valuable classification information and enhancing the robustness against noise/outliers. To enhance consensus, ERL-MVSC learns a shared predicted label matrix, which serves as the comment target of multi-view classification. With these techniques, we formulate ERL-MVSC as a joint optimization problem of an embedding regularizer and a predicted label matrix, which can be solved by a coordinate descent method. Extensive experimental results on real-world datasets demonstrate the effectiveness and superiority of the proposed algorithm.