Multi-view Spectral Clustering Based on Low-rank Tensor Decomposition

Xiao, Qingjiang; Du, Shiqiang; Huang, Yixuan

doi:10.1109/ccdc52312.2021.9602065

Cited by 35 publications

(69 citation statements)

References 18 publications

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“…A common limitation to the proposed multi-view clustering methods is that they do not consider to deal with possibly noisy or corrupted data, because they focus on the consistency of multiple layers and do not consider the inconsistency. To address this issue, Xia et al [48] proposed Robust Multi-view Spectral Clustering, a Markov chain method that aims to learn an intrinsic transition matrix from multiple views by restricting the transition matrix to be low-rank. This aspect has also been considered by Mercado et al [28] [27], where they propose a Laplacian operator obtained by merging the Laplacians from different layers via a one-parameter family of nonlinear matrix power means.…”

Section: Related Workmentioning

confidence: 99%

Louvain-like Methods for Community Detection in Multiplex Networks

Venturini¹,

Cristofari²,

Rinaldi³

et al. 2021

Preprint

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In many complex systems, entities interact with each other through complicated patterns that embed different relationships, thus generating networks with multiple levels and/or multiple types of edges. When trying to improve our understanding of those complex networks, it is of paramount importance to explicitly take the multiple layers of connectivity into account in the analysis. In this paper, we focus on detecting community structures in multi-layer networks, i.e., detecting groups of well-connected nodes shared among the layers, a very popular task that poses a lot of interesting questions and challenges. Most of the available algorithms in this context either reduce multi-layer networks to a single-layer network or try to extend algorithms for single-layer networks by using consensus clustering. Those approaches have anyway been criticized lately. They indeed ignore the connections among the different layers, hence giving low accuracy. To overcome these issues, we propose new community detection methods based on tailored Louvain-like strategies that simultaneously handle the multiple layers. We consider the informative case, where all layers show a community structure, and the noisy case, where some layers only add noise to the system. We report experiments on both artificial and real-world networks showing the effectiveness of the proposed strategies.

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Section: Related Workmentioning

confidence: 99%

Louvain-like Methods for Community Detection in Multiplex Networks

Venturini¹,

Cristofari²,

Rinaldi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To solve this problem, the multi-view learning with adaptive neighbours (MLAN) [3] and the selfweighted multi-view clustering (SwMC) [4] are proposed. To capture the shared information among the graphs of multiple views, Xia et al proposed the robust multi-view spectral clustering (RMSC) [13]. To well characterizes high-order information embedded in view-similar graph, Wu et al [14] proposed essential tensor learning for multi-view spectral clustering (ETLMSC) method.…”

Section: Related Workmentioning

confidence: 99%

“…1) MSRC-v5 [22] is an object dataset which has seven categories of tree, building, airplane, cow, face, car and bicycle and each of them contains 30 images. There are 5 views as reported in the paper [16] which respectively Baselines: We choose 12 comparisons, including singleview and multi-view methods, i.e., Co-reg [1], SwMC [4], MVGL [6], MVSC [15], RDEKM [5], SMSC [16], AMGL [12], MLAN [3], RMSC [26], SFMC [2], CSMSC [27] and singleview constrained Laplacian rank (s-CLR) [28].…”

Section: A Experimental Setupmentioning

confidence: 99%

Multiple Graph Learning for Scalable Multi-view Clustering

Jiang,

Gao,

Gao

2021

Preprint

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Graph-based multi-view clustering has become an active topic due to the efficiency in characterizing both the complex structure and relationship between multimedia data. However, existing methods have the following shortcomings: (1) They are inefficient or even fail for graph learning in large scale due to the graph construction and eigen-decomposition. (2) They cannot well exploit both the complementary information and spatial structure embedded in graphs of different views. To well exploit complementary information and tackle the scalability issue plaguing graph-based multi-view clustering, we propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer. Finally, we develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model. Extensive experimental results on several datasets indicate that our proposed method outperforms some state-of-the-art multi-view clustering algorithms.

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“…For multi-view subspace clustering, more attention is paid to how to boost the clustering accuracy by fully exploring the complementary information carried by multi-view data. For instance, multi-view low-rank sparse subspace clustering (MLRSSC) [59] harnessing both low-rank and sparsity constraints shows much better performance than previous methods, such as [60]. Multi-view subspace clustering with intactness-aware similarity (MSC IAS) [61] tries to construct a graph in latent space, which leads to superior accuracy.…”

Section: A Subspace Clusteringmentioning

confidence: 99%

Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view

Kang

Lin

Zhu³

et al. 2021

Preprint

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Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: encounter the expensive time overhead, fail in exploring 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 a 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 multi-view data is also proposed, which is linear 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.

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Multi-view Spectral Clustering Based on Low-rank Tensor Decomposition

Cited by 35 publications

References 18 publications

Louvain-like Methods for Community Detection in Multiplex Networks

Louvain-like Methods for Community Detection in Multiplex Networks

Multiple Graph Learning for Scalable Multi-view Clustering

Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view

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