Multi-view Subspace Clustering

Gao, Hongchang; Nie, Feiping; Li, Xuelong; Huang, Heng

doi:10.1109/iccv.2015.482

Cited by 422 publications

(164 citation statements)

References 20 publications

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“…Gao et al [23] proposed multi-view subspace clustering algorithm. It learns a graph for each view and enforces a common cluster indicator matrix for all graphs.…”

Section: Introductionmentioning

confidence: 99%

Multi-graph fusion for multi-view spectral clustering

Kang

Shi

Huang

et al. 2020

Knowledge-Based Systems

208

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A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and demonstrated promising results recently. Despite progress, there are still two fundamental questions that stay unanswered to date. First, how to fuse different views into one graph. More often than not, the similarities between samples may be manifested differently by different views.Many existing algorithms either simply take the average of multiple views or just learn a common graph. These simple approaches fail to consider the flexible local manifold structures of all views. Hence, the rich heterogeneous information is not fully exploited. Second, how to learn the explicit cluster structure. Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately. Those unreliable graphs might lead to suboptimal clustering results. To fill these gaps, in this paper, we propose a novel multi-view spectral clustering model which performs graph fusion and spectral clustering simultaneously. The fusion graph approximates the original graph of each individual view but maintains an explicit cluster structure. Experiments on four widely used data sets confirm the superiority of the proposed method.

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“…Gao et al [23] proposed multi-view subspace clustering algorithm. It learns a graph for each view and enforces a common cluster indicator matrix for all graphs.…”

Section: Introductionmentioning

confidence: 99%

Multi-graph fusion for multi-view spectral clustering

Kang

Shi

Huang

et al. 2020

Knowledge-Based Systems

208

View full text Add to dashboard Cite

show abstract

“…Traditional clustering aims to identify groups of "similar behavior" in single view data [29,30,31,32,33,34]. As the real-world data are always captured from multiple sources or represented by several distinct feature sets [36,37,38,39,40], multi-view clustering is intensively studied recently by leveraging the heterogeneous data to achieve the same goal. Different features characterize different information from the data set.…”

Section: Discussionmentioning

confidence: 99%

“…Multi-view clustering aims to integrate multiple feature sets together, and uncover the consistent latent information from different views. Extensive research efforts have been made in developing effective methods [36,38,41,42]. A good survey can be found in [35].…”

Section: Discussionmentioning

confidence: 99%

“…As the real-world data are always captured from multiple sources or represented by several distinct feature sets [35,36,37,38,39,40], multi-view clustering is intensively studied recently by leveraging the heterogeneous data to achieve the same goal [36,38,41,42].…”

Section: Multi-view Subspace Clusteringmentioning

confidence: 99%

“…Gao et al proposed to perform clustering on the subspace representation of each view simultaneously guided by a common cluster structure for the consistence across different views [38].…”

Section: Multi-view Subspace Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

Robust unsupervised subspace learning for visual representation

Zhao¹

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Pseudolabel‐guided multiview consensus graph learning for semisupervised classification

Guo

Wang

2022

Int J of Intelligent Sys

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Semisupervised multiview learning gains extensive research attention due to its strong capability to utilize the heterogeneous features and the label information of a few labeled samples. However, the supervision information is not well utilized in the process of exploring the consensus structure of the multiview data. In this paper, we propose a novel unified pseudolabel-guided multiview consensus (PMvC) learning framework for the semisupervised classification problem, which learns the consensus structure of multiview data by fully exploiting the supervised information of labeled samples. Specifically, PMvC first assigns multiple pseudolabels to the unlabeled samples by selecting the nearest labeled sample in each view separately, and then labels the part of unlabeled samples by selecting the pseudolabel that agrees across all views. By doing so, the high-confident pseudolabeled samples can be selected to enlarge the labeled sample pool and the supervision information can be exploited further in the learning process. In addition, to capture the consensus structure of the multiview data, PMvC learns a consensus graph from the view-specific selfrepresentation graph guided by enhanced supervision information, which better preserves the manifold structure of samples. Meanwhile, the label information is also propagated from the labeled samples to the unlabeled samples by the learned consensus graph simultaneously.

show abstract

Multi-view Subspace Clustering

Cited by 422 publications

References 20 publications

Multi-graph fusion for multi-view spectral clustering

Multi-graph fusion for multi-view spectral clustering

Robust unsupervised subspace learning for visual representation

Pseudolabel‐guided multiview consensus graph learning for semisupervised classification

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