Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization

By Yang Wang, Lin Wu, Xuemin Lin, Junbin Gao. IEEE Transactions on Neural Networks and Learning Systems, 2017

375 citations recorded by the source corpus.

Multiview data clustering attracts more attention than their single-view counterparts due to the fact that leveraging multiple independent and complementary information from multiview feature spaces outperforms the single one. Multiview spectral clustering aims at yielding the data partition agreement over their local manifold structures by seeking eigenvalue–eigenvector decompositions. Among all the methods, low-rank representation (LRR) is effective, by exploring the multiview consensus structures beyond the low rankness to boost the clustering performance. However, as we observed, such classical paradigm still suffers from the following stand-out limitations for multiview spectral clustering of overlooking the flexible local manifold structure, caused by aggressively enforcing the low-rank data correlation agreement among all views, and such a strategy, therefore, cannot achieve the satisfied between-views agreement; worse still, LRR is not intuitively flexible to capture the latent data clustering structures. In this paper, first, we present the structured LRR by factorizing into the latent low-dimensional data-cluster representations, which characterize the data clustering structure for each view. Upon such representation, second, the Laplacian regularizer is imposed to be capable of preserving the flexible local manifold structure for each view. Third, we present an iterative multiview agreement strategy by minimizing the divergence objective among all factorized latent data-cluster representations during each iteration of optimization process, where such latent representation from each view serves to regulate those from other views, and such an intuitive process iteratively coordinates all views to be agreeable. Fourth, we remark that such data-cluster representation can flexibly encode the data clustering structure from any view with an adaptive input cluster number. To this end, finally, a novel nonconvex objective function is proposed via the efficient alternating minimization strategy. The complexity analysis is also presented. The extensive experiments conducted against the real-world multiview data sets demonstrate the superiority over the state of the arts.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/8e57b037ccf2e45477221d600e36626b6710ab7e

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Citations in the corpus

Essential Tensor Learning for Multi-View Spectral Clustering (2018, methodology): “ a Markov mixture. Considering that multi-view data might be noisy, RMSC [6] hopes to recover a shared low-rank transition probability matrix for the Markov chain based spectral clustering. Recently, [26] proposes the structured low-rank matrix factorization methods for multi-view spectral clusterin”
Applications of Generative Adversarial Networks (GANs): An Updated Review (2019, background): “[16, 117, 118, 138] and (2) developing a distinctive signature for representing a person under different camera environment having classification typically on cross-image representation [2, 111, 124].”
Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, background): “Since low-rank prior is effective to learn the data redundancies and self-similarities, low-rank learning has been popularly used in matrix completion [20], image fusion [21], clustering [22], and pattern recognition [23].”
Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, methodology): “In [14], a structured low-rank matrix factorization-based method is proposed to learn more consistent low-dimensional data-cluster representations for all views by exploiting the manifold structures and introducing the divergence constraint term jointly.”
GMC: Graph-Based Multi-View Clustering (2020, background): “Our work is also related to multi-view spectral clustering [18], [19], [20], [21], [22], [23], [24], [25].”