Diversity-induced Multi-view Subspace Clustering

Cao, Xiaochun; Zhang, Changqing; Fu, Huazhu; Liu, Si; Zhang, Hua

doi:10.1109/cvpr.2015.7298657

Cited by 548 publications

(288 citation statements)

References 20 publications

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“…Zhang et al [16] also developed MMNMF which attempted to preserve intrinsic geometrical structure of data across multiple views. Cao et al [5] utilized a diversity constraints on subspaces to enhance the complementarity among multiple views.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

Wang

Tian

et al. 2016

Neural Information Processing

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Abstract. Multi-view clustering, which explores complementary information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF), which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced. Besides, AMVNMF provides a viable solution to learn the weight of each view adaptively with only a single parameter. Using L2,1-norm, AMVNMF is also robust to noises and outliers. We further develop an efficient iterative algorithm for solving the optimization problem. Experiments carried out on five well-known datasets have demonstrated the effectiveness of AMVNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information.

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Section: Introductionmentioning

confidence: 99%

Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

Wang

Tian

et al. 2016

Neural Information Processing

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“…For these datasets, we follow the preprocessing strategy [127]. Firstly all the images are resized into 48 ⇥ 48 and then three kinds of features are extracted, i.e., intensity, LBP [128] and…”

Section: Algorithm 5: Optimization Of Problem (43)mentioning

confidence: 99%

“…• NaMSC [127] firstly applies [132] to each view data, then combines the learned representations and feeds to the spectral clustering.…”

Section: Algorithm 5: Optimization Of Problem (43)mentioning

confidence: 99%

“…• DiMSC [127] investigates the complementary information of representations of multi-view data by introducing a diversity term. This work is also one of the most recent approaches in MVC.…”

Section: Algorithm 5: Optimization Of Problem (43)mentioning

confidence: 99%

“…For details about the metrics, readers could refer to [130,127]. For all the metrics, higher value denotes better performance.…”

Section: Algorithm 5: Optimization Of Problem (43)mentioning

confidence: 99%

See 2 more Smart Citations

Robust unsupervised subspace learning for visual representation

Zhao¹

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Multiview spectral clustering via complementary information

Liu

Zheng

et al. 2020

Concurrency and Computation

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In this article, multiview spectral clustering via complementary information (MSCC) is proposed, in which both the consensus information and the complementary information are explored for multiview clustering. In contrast to most multiview spectral clustering methods, the proposed MSCC considers the differences among multiple views and constructs a similarity matrix for clustering. Furthermore, a convex relaxation is employed and an algorithm that is based on the augmented Lagrange multiplier is proposed for optimizing the objective function of MSCC. In extensive experiments on five real‐world benchmark datasets, our proposed method outperforms two baselines and has significantly improved to several state‐of‐the‐art multiview clustering methods.

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Diversity-induced Multi-view Subspace Clustering

Cited by 548 publications

References 20 publications

Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

Robust unsupervised subspace learning for visual representation

Multiview spectral clustering via complementary information

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