Multi-view clustering via pairwise sparse subspace representation

Yin, Qiyue; Wu, Shu; He, Ran; Wang, Liang

doi:10.1016/j.neucom.2015.01.017

Cited by 111 publications

(38 citation statements)

References 26 publications

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“…From Eq. (27) we see that each cluster may have different centers and that the points in each cluster can have different dispersions in different feature groups. We can change the input standard deviation matrix B to generate subspace clusters in feature groups.…”

Section: Synthetic Data Generationmentioning

confidence: 85%

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Subspace clustering with automatic feature grouping

Gan

2015

Pattern Recognition

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Section: Synthetic Data Generationmentioning

confidence: 85%

“…It is also worth to point out that FG-k-means and AFG-k-means are related to multi-view clustering [26,27]. The feature groups are similar to the views in multi-view clustering.…”

Section: Discussionmentioning

confidence: 98%

Subspace clustering with automatic feature grouping

Gan

2015

Pattern Recognition

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“…Recently, Shao et al [37] proposed a online multi-view clustering method with incomplete view via imposing lasso regularization on the representation of each view. More references can be referred to [16,33,40,50]. These methods are useful for the nonnegative multiview data analysis, however, they are not suitable for the noisy views and incomplete views, which are often encountered in real applications.…”

Section: Multi-viewmentioning

confidence: 99%

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

Long

Tan

et al. 2017

Multimed Tools Appl

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With the increasing availability of multiview nonnegative data in real applications, multiview representation learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions. However, existing NMF-based methods are sensitive to noises and are difficult to generate discriminative features with noisy views. To address these problems, we propose a co-regularized multiview nonnegative matrix factorization method with correlation constraint for nonnegative representation learning, which jointly exploits consistent and complementary information across different views. Different from previous works, we aim at integrating information from multiple views efficiently and making it more robust to the presence of noisy views. More specifically, we exploit the complementary information of multiple views through the co-regularization to accommodate the presence of the noisy views. Meanwhile, correlation constraint is imposed on the low-dimensional space to learn a common latent representation shared by different views. For the induced objective function, we derive an alternative algorithm to solve the optimization problem. The experimental results on four real datasets demonstrate the effectiveness and robustness of the proposed algorithm.

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“…To address this problem, subspace learning based methods [23,27] are the most studied approaches. Classical subspace learning algorithms such as the Canonical Correlation Analysis (CCA) [10] and the Partial Least Squares (PLS) [19] have been adopted for learning a common representation for heterogeneous modalities.…”

Section: Introductionmentioning

confidence: 99%

Principal affinity based cross-modal retrieval

Liang

Cao

et al. 2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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Multimedia content is increasingly available in multiple modalities. Each modality provides a different representation of the same entity. This paper studies the problem of joint representation of the text and image components of multimedia documents. However, most existing algorithms focus more on inter-modal connection rather than intramodal feature extraction. In this paper, a simple yet effective principal affinity representation (PAR) approach is proposed to exploit the affinity representations of different modalities with local cluster samples. Afterwards, multiclass logistic regression model is adopted to learn the projections from principal affinity representation to semantic labels vectors. Inner product distance is further used to improve cross-modal retrieval performance. Extensive experiments on three benchmark datasets illustrate that our proposed method obtains significant improvements over the state-of-the-art subspace learning based cross-modal methods.

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Multi-view clustering via pairwise sparse subspace representation

Cited by 111 publications

References 26 publications

Subspace clustering with automatic feature grouping

Subspace clustering with automatic feature grouping

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

Principal affinity based cross-modal retrieval

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