Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed and achieved success in real-world applications, most of which assume that all views share a same coefficient matrix. However, the underlying information of multiview data are not exploited effectively under this assumption, since the coefficient matrices of different views should have the same clustering properties rather than be the same among multiple views. To this end, a novel Constrained Bilinear Factorization Multi-view Subspace Clustering (CBF-MSC) method is proposed in this paper. Specifically, the bilinear factorization with an orthonormality constraint and a low-rank constraint is employed for all coefficient matrices to make all coefficient matrices have the same trace-norm instead of being equivalent, so as to explore the consensus information of multi-view data more effectively. Finally, an algorithm based on the Augmented Lagrangian Multiplier (ALM) scheme with alternating direction minimization is designed to optimize the objective function. Comprehensive experiments tested on six benchmark datasets validate the effectiveness and competitiveness of the proposed approach compared with several state-of-the-art approaches.
Generally, multi-view clustering approaches can be categorized into two main groups roughly: generative methods and discriminative methods @cite_6 . Methods in the first category try to construct generative models for multiple views by learning the fundamental distribution of data. For instance, the work proposed in @cite_27 explores the multi-view clustering problem under an assumption that views are dependent and multi-nomial distribution is applied for models construction; the multi-view clustering method in @cite_20 learns convex mixture models for multiple views.
@cite_27 @cite_20 @cite_6
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Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead to bad cluster assignments if a view is of poor quality. To deal with this problem, we propose a method that is built upon exemplar-based mixture models, called convex mixture models (CMMs). More specifically, we present a multiview clustering algorithm, based on training a weighted multiview CMM, that associates a weight with each view and learns these weights automatically. Our approach is computationally efficient and easy to implement, involving simple iterative computations. Experiments with several datasets confirm the advantages of assigning weights to the views and the superiority of our framework over single-view and unweighted multiview CMMs, as well as over another multiview algorithm which is based on kernel canonical correlation analysis. In past decade, more and more data are collected from multiple sources or represented by multiple views, where different views describe distinct perspectives of the data. Although each view could be individually used for finding patterns by clustering, the clustering performance could be more accurate by exploring the rich information among multiple views. Several multi-view clustering methods have been proposed to unsupervised integrate different views of data. However, they are graph based approaches, e.g. based on spectral clustering, such that they cannot handle the large-scale data. How to combine these heterogeneous features for unsupervised large-scale data clustering has become a challenging problem. In this paper, we propose a new robust large-scale multi-view clustering method to integrate heterogeneous representations of largescale data. We evaluate the proposed new methods by six benchmark data sets and compared the performance with several commonly used clustering approaches as well as the baseline multi-view clustering methods. In all experimental results, our proposed methods consistently achieve superiors clustering performances. Multi-view clustering, which seeks a partition of the data in multiple views that often provide complementary information to each other, has received considerable attention in recent years. In real life clustering problems, the data in each view may have considerable noise. However, existing clustering methods blindly combine the information from multi-view data with possibly considerable noise, which often degrades their performance. In this paper, we propose a novel Markov chain method for Robust Multi-view Spectral Clustering (RMSC). Our method has a flavor of lowrank and sparse decomposition, where we firstly construct a transition probability matrix from each single view, and then use these matrices to recover a shared low-rank transition probability matrix as a crucial input to the standard Markov chain method for clustering. The optimization problem of RMSC has a low-rank constraint on the transition probability matrix, and simultaneously a probabilistic simplex constraint on each of its rows. To solve this challenging optimization problem, we propose an optimization procedure based on the Augmented Lagrangian Multiplier scheme. Experimental results on various real world datasets show that the proposed method has superior performance over several state-of-the-art methods for multi-view clustering. Multi-view clustering, which aims to cluster datasets with multiple sources of information, has a wide range of applications in the communities of data mining and pattern recognition. Generally, it makes use of the complementary information embedded in multiple views to improve clustering performance. Recent methods usually find a low-dimensional embedding of multi-view data, but often ignore some useful prior information that can be utilized to better discover the latent group structure of multi-view data. To alleviate this problem, a novel pairwise sparse subspace representation model for multi-view clustering is proposed in this paper. The objective function of our model mainly includes two parts. The first part aims to harness prior information to achieve a sparse representation of each high-dimensional data point with respect to other data points in the same view. The second part aims to maximize the correlation between the representations of different views. An alternating minimization method is provided as an efficient solution for the proposed multi-view clustering algorithm. A detailed theoretical analysis is also conducted to guarantee the convergence of the proposed method. Moreover, we show that the must-link and cannot-link constraints can be naturally integrated into the proposed model to obtain a link constrained multi-view clustering model. Extensive experiments on five real world datasets demonstrate that the proposed model performs better than several state-of-the-art multi-view clustering methods.