LABIN: Balanced Min Cut for Large-Scale Data

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.

Parameter-Insensitive Min Cut Clustering With Flexible Size Constrains

Clustering is a fundamental topic in machine learning and various methods are proposed, in which K-Means (KM) and min cut clustering are typical ones. However, they may produce empty or skewed clustering results, which are not as expected. In KM, the constrained clustering methods have been fully studied while in min cut clustering, it still needs to be developed. In this paper, we propose a parameter-insensitive min cut clustering with flexible size constraints. Specifically, we add lower limitations on the number of samples for each cluster, which can perfectly avoid the trivial solution in min cut clustering. As far as we are concerned, this is the first attempt of directly incorporating size constraints into min cut. However, it is a NP-hard problem and difficult to solve. Thus, the upper limits is also added in but it is still difficult to solve. Therefore, an additional variable that is equivalent to label matrix is introduced in and the augmented Lagrangian multiplier (ALM) is used to decouple the constraints. In the experiments, we find that the our algorithm is less sensitive to lower bound and is practical in image segmentation. A large number of experiments demonstrate the effectiveness of our proposed algorithm.

Discrete and Balanced Spectral Clustering With Scalability

Spectral Clustering (SC) has been the main subject of intensive research due to its remarkable clustering performance. Despite its successes, most existing SC methods suffer from several critical issues. First, they typically involve two independent stages, i.e., learning the continuous relaxation matrix followed by the discretization of the cluster indicator matrix. This two-stage approach can result in suboptimal solutions that negatively impact the clustering performance. Second, these methods are hard to maintain the balance property of clusters inherent in many real-world data, which restricts their practical applicability. Finally, these methods are computationally expensive and hence unable to handle large-scale datasets. In light of these limitations, we present a novel Discrete and Balanced Spectral Clustering with Scalability (DBSC) model that integrates the learning the continuous relaxation matrix and the discrete cluster indicator matrix into a single step. Moreover, the proposed model also maintains the size of each cluster approximately equal, thereby achieving soft-balanced clustering. What's more, the DBSC model incorporates an anchor-based strategy to improve its scalability to large-scale datasets. The experimental results demonstrate that our proposed model outperforms existing methods in terms of both clustering performance and balance performance. Specifically, the clustering accuracy of DBSC on CMUPIE data achieved a 17.93% improvement compared with that of the SOTA methods (LABIN, EBSC, etc.).

Spectral Clustering With Adaptive Neighbors for Deep Learning

Spectral clustering is a well-known clustering algorithm for unsupervised learning, and its improved algorithms have been successfully adapted for many real-world applications. However, traditional spectral clustering algorithms are still facing many challenges to the task of unsupervised learning for large-scale datasets because of the complexity and cost of affinity matrix construction and the eigen-decomposition of the Laplacian matrix. From this perspective, we are looking forward to finding a more efficient and effective way by adaptive neighbor assignments for affinity matrix construction to address the above limitation of spectral clustering. It tries to learn an affinity matrix from the view of global data distribution. Meanwhile, we propose a deep learning framework with fully connected layers to learn a mapping function for the purpose of replacing the traditional eigen-decomposition of the Laplacian matrix. Extensive experimental results have illustrated the competitiveness of the proposed algorithm. It is significantly superior to the existing clustering algorithms in the experiments of both toy datasets and real-world datasets.

Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview

Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: they encounter the expensive time overhead, they fail to explore the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable graph learning framework, seeking to address the above three challenges simultaneously. Specifically, it is based on the ideas of anchor points and bipartite graph. Rather than building an n×n graph, where n is the number of samples, we construct a bipartite graph to depict the relationship between samples and anchor points. Meanwhile, a connectivity constraint is employed to ensure that the connected components indicate clusters directly. We further establish the connection between our method and the K -means clustering. Moreover, a model to process multiview data is also proposed, which is linearly scaled with respect to n . Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.

Fast Self-Supervised Clustering With Anchor Graph

Benefit from avoiding the utilization of labeled samples, which are usually insufficient in the real world, unsupervised learning has been regarded as a speedy and powerful strategy on clustering tasks. However, clustering directly from primal data sets leads to high computational cost, which limits its application on large-scale and high-dimensional problems. Recently, anchor-based theories are proposed to partly mitigate this problem and field naturally sparse affinity matrix, while it is still a challenge to get excellent performance along with high efficiency. To dispose of this issue, we first presented a fast semisupervised framework (FSSF) combined with a balanced K -means-based hierarchical K -means (BKHK) method and the bipartite graph theory. Thereafter, we proposed a fast self-supervised clustering method involved in this crucial semisupervised framework, in which all labels are inferred from a constructed bipartite graph with exactly k connected components. The proposed method remarkably accelerates the general semisupervised learning through the anchor and consists of four significant parts: 1) obtaining the anchor set as interim through BKHK algorithm; 2) constructing the bipartite graph; 3) solving the self-supervised problem to construct a typical probability model with FSSF; and 4) selecting the most representative points regarding anchors from BKHK as an interim and conducting label propagation. The experimental results on toy examples and benchmark data sets have demonstrated that the proposed method outperforms other approaches.