Multiple Partitions Aligned Clustering

Kang, Zhao; Guo, Zipeng; Huang, Shudong; Wang, Siying; Chen, Wenyu; Su, Yuanzhang; Xu, Zenglin

doi:10.24963/ijcai.2019/375

Cited by 45 publications

(17 citation statements)

References 2 publications

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“…shared information of multi-view data, and perform clustering therein (Li, Jiang, and Zhou 2014;Gao et al 2015;Zhao, Ding, and Fu 2017;Zong et al 2017;Kang et al 2019).…”

Section: Factorization Factorization and Diversity Controlmentioning

confidence: 99%

Multi-View Multiple Clusterings Using Deep Matrix Factorization

Wei

Wang

et al. 2020

AAAI

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Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the data. Due to their multiplicity, multi-view data, can have different groupings that are reasonable and interesting from different perspectives. However, how to find multiple, meaningful, and diverse clustering results from multi-view data is still a rarely studied and challenging topic in multi-view clustering and multiple clusterings. In this paper, we introduce a deep matrix factorization based solution (DMClusts) to discover multiple clusterings. DMClusts gradually factorizes multi-view data matrices into representational subspaces layer-by-layer and generates one clustering in each layer. To enforce the diversity between generated clusterings, it minimizes a new redundancy quantification term derived from the proximity between samples in these subspaces. We further introduce an iterative optimization procedure to simultaneously seek multiple clusterings with quality and diversity. Experimental results on benchmark datasets confirm that DMClusts outperforms state-of-the-art multiple clustering solutions.

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“…shared information of multi-view data, and perform clustering therein (Li, Jiang, and Zhou 2014;Gao et al 2015;Zhao, Ding, and Fu 2017;Zong et al 2017;Kang et al 2019).…”

Section: Factorization Factorization and Diversity Controlmentioning

confidence: 99%

Multi-View Multiple Clusterings Using Deep Matrix Factorization

Wei

Wang

et al. 2020

AAAI

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“…There has been growing interest in developing multiview subspace clustering (MVSC) algorithms. For example, MVSC (Gao et al 2015) method learns a graph representation for each view and all graphs are assumed to share a unique cluster matrix; (Cao et al 2015) applies a diversity term to explore the complementarity information; (Luo et al 2018) explores both consistency and specificity of different views; (Wang et al 2019;Zhang et al 2017) perform MVSC in latent representation; (Brbić and Kopriva 2018) imposes both low-rank and sparsity constraints on the graph; (Kang et al 2019a;2020) learn one partition for each view and them perform a fusion in partition space. In terms of clustering accuracy, these methods are attractive.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Multi-View Subspace Clustering in Linear Time

Kang

Zhou

Zhao

et al. 2020

AAAI

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258

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A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.

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“…How to utilize them to build a better model is crucial and valuable in real-world applications. Current multi-view, also known as multimodal, approaches address this issue by sufficiently exploring their complementary information [13,14,20,21,32,33,39]. However, the collected data are mostly incomplete in practice due to sensor failure or human error.…”

Section: Introductionmentioning

confidence: 99%

Self-Representation Subspace Clustering for Incomplete Multi-view Data

Liu

Zhang

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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Incomplete multi-view clustering is an important research topic in multimedia where partial data entries of one or more views are missing. Current subspace clustering approaches mostly employ matrix factorization on the observed feature matrices to address this issue. Meanwhile, self-representation technique is left unexplored, since it explicitly relies on full data entries to construct the coefficient matrix, which is contradictory to the incomplete data setting. However, it is widely observed that self-representation subspace method enjoys a better clustering performance over the factorization based one. Therefore, we adapt it to incomplete data by jointly performing data imputation and self-representation learning. To the best of our knowledge, this is the first attempt in incomplete multi-view clustering literature. Besides, the proposed method is carefully compared with current advances in experiment with respect to different missing ratios, verifying its effectiveness. CCS CONCEPTS• Computing methodologies → Cluster analysis; Statistical relational learning; Spectral methods; • Theory of computation → Unsupervised learning and clustering; • Mathematics of computing → Nonconvex optimization.

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Multiple Partitions Aligned Clustering

Cited by 45 publications

References 2 publications

Multi-View Multiple Clusterings Using Deep Matrix Factorization

Multi-View Multiple Clusterings Using Deep Matrix Factorization

Large-Scale Multi-View Subspace Clustering in Linear Time

Self-Representation Subspace Clustering for Incomplete Multi-view Data

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