Double Constrained NMF for Partial Multi-View Clustering

Qian, Bin; Shen, Xiaobo; Gu, Yanyang; Tang, Zhenmin; Ding, Yongsheng

doi:10.1109/dicta.2016.7797034

Cited by 33 publications

(15 citation statements)

References 19 publications

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“…By introducing the binary weight to regularize the data reconstruction, the locality structure of the original data in each view can be well preserved. Meanwhile, from (2), we can find that the proposed method does not introduce any extra regularization term and corresponding tuned parameter to preserve such locality property, which greatly reduces the complexity of penalty parameter selection in comparison with the other graph regularized IMC methods, such as DCNMF [13] and GPMVC [16] which all commonly introduce at least an extra tuned penalty parameter to preserve such locality property. For the paired samples across different views, their new representation should be consensus.…”

Section: Learning Model Of the Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization

Wen

Zhang

et al. 2019

Lecture Notes in Computer Science

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Clustering with incomplete views is a challenge in multi-view clustering. In this paper, we provide a novel and simple method to address this issue. Specially, the proposed method simultaneously exploits the local information of each view and the complementary information among views to learn the common latent representation for all samples, which can greatly improve the compactness and discriminability of the obtained representation. Compared with the conventional graph embedding methods, the proposed method does not introduce any extra regularization term and corresponding penalty parameter to preserve the local structure of data, and thus does not increase the burden of extra parameter selection. By imposing the orthogonal constraint on the basis matrix of each view, the proposed method is able to handle the outof-sample. Moreover, the proposed method can be viewed as a unified framework for multi-view learning since it can handle both incomplete and complete multi-view clustering and classification tasks. Extensive experiments conducted on several multi-view datasets prove that the proposed method can significantly improve the clustering performance.

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Section: Learning Model Of the Proposed Methodsmentioning

confidence: 99%

“…while not converged do for k from 1 to v Update U (k) using (9). Update P (k) using (13). end Update P c using (15).…”

Section: Algorithm 1 : Imc Grmf (Solving Problem (4))mentioning

confidence: 99%

Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization

Wen

Zhang

et al. 2019

Lecture Notes in Computer Science

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show abstract

“…In ), a partial multi-modal sparse coding framework was proposed to exploit the similarity structure within the same modality and between different modalities. (Qian et al 2016) developed a double constrained framework called DCNMF by incorporating the cluster similarity and manifold preserving constraints. (Gao, Peng, and Jian 2016) gave an IVC algorithm for clustering with more than two incomplete views, which was based on spectral graph theory and kernel alignment principle.…”

Section: Related Workmentioning

confidence: 99%

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Guo

2019

AAAI

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Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research problem of partial multi-view clustering. To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering. APMC is conceptually simple and easy to implement in practice, besides it has clear intuitions and non-trivial empirical guarantees. Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result. Compared with existing partial multi-view clustering methods, APMC has three notable advantages: 1) it can capture more non-linear relations among instances with the help of kernel-based similarities; 2) it has a much lower time complexity in virtue of a noniterative scheme; 3) it can inherently handle data with negative entries as well as be extended to more than two views. Finally, we extensively evaluate the proposed method on five benchmark datasets. Experimental results demonstrate the superiority of APMC over state-of-the-art approaches.

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“…Researchers have proposed a variety of methods for partial multiview clustering [10][11][12][13][14][15][16][17][18][19][20], which can be generally divided into two categories: matrix factorization-based clustering method and graph-based clustering method. The matrix factorization technique directly learns the low dimensional consistent representation of all views.…”

Section: Introductionmentioning

confidence: 99%

“…IMG [15] integrates latent subspace generation and compact global structure into a unified framework through a Laplacian graph on a complete data instance, and this integration brings more parameters. DCNMF [16] develops a dual constraint framework by combining cluster similarity and manifold keeping constraints. Gao et al [17] proposed to fill the missing views with the average of the instances in the corresponding views for the construction of the graph and the learning of the subspace.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Anchor-Based Partial Multiview Clustering

Lei

Sheng

2020

IEEE Access

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Partial multiview clustering, which aims to effectively merge multiple prespecified incomplete views to improve clustering performance, is a research hotspot and difficulty in the field of machine learning. Guo et al. proposed a partial multiview clustering method (APMC) based on anchor graph, which uses a Gauss kernel function to solve the similarity matrix. The Gaussian kernel function is sensitive to parameter σ, and it is difficult to find the optimal value only by stepwise adjustment in practical applications. This undoubtedly affects the practicality of the APMC algorithm. To address this issue, an adaptive partial multiview clustering method based on anchor graph (AAPMC) is developed in this paper, which proposes an adaptive neighbor assignment strategy and utilizes it to improve the anchor-based similarity matrix computation of each view. The method proposed in this paper uses an adaptive method to solve the similarity matrix, eliminating the tediousness of parameter adjustment. In addition, the non-iterative method based on anchors is used to solve the optimal solution with low time complexity and is suitable for large-scale datasets. In short, our method is simple and effective, and it is easier to implement in practice. Extensive experiments show that our model can not only effectively solve the parameter setting problem, but also performs better than the state-of-the-art partial multiview clustering methods.

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Double Constrained NMF for Partial Multi-View Clustering

Cited by 33 publications

References 19 publications

Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization

Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Adaptive Anchor-Based Partial Multiview Clustering

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