Deep Multi-View Subspace Clustering With Unified and Discriminative Learning

Wang, Qianqian; Cheng, Jiafeng; Gao, Quanxue; Zhao, Guoshuai; Jiao, Licheng

doi:10.1109/tmm.2020.3025666

Cited by 114 publications

(33 citation statements)

References 38 publications

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“…Regarding the optimisation of U ðvÞ C , U ðvÞ I , V C , V ðvÞ I , their Lagrangian functions are constructed, respectively. By applying the Karush Kuhn Tucker (KKT) conditions in each Lagrangian function, the following update rules for each matrix variable can be achieved: Regarding the non-incremental NMF-based algorithms [7,8,11,14,17,18,28] mentioned above, they require the whole dataset when executing. Therefore, they will cost much time learning the common feature of each new incoming multiview instance.…”

Section: Non-incremental Clustering Methodsmentioning

confidence: 99%

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Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

Zhao

Yang

Zhang

et al. 2021

IET Computer Vision

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In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi‐view data becomes a challenging problem. Some single‐view methods focus on processing the data dynamically, but they are not suitable for multi‐view data. Some online multi‐view methods are proposed to tackle it, but they ignore the influence of uncorrelated items in each view. Therefore, in this study, the authors propose a new algorithm, called Incremental Multi‐view Correlated Feature Learning (IMCFL) based on non‐negative matrix factorisation, to learn the common feature across views. By separating uncorrelated items of new instances and constructing incremental joint learning of correlated and uncorrelated features, the proposed IMCFL can eliminate the influence of uncorrelated information in the individual view and improve the effectiveness of incremental multi‐view common feature learning. Extensive experiments on real‐world datasets confirm its superiority by comparing it with other state‐of‐the‐art incremental and non‐incremental methods.

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Section: Non-incremental Clustering Methodsmentioning

confidence: 99%

“…Regarding the non‐incremental NMF‐based algorithms [7, 8, 11, 14, 17, 18, 28] mentioned above, they require the whole dataset when executing. Therefore, they will cost much time learning the common feature of each new incoming multi‐view instance.…”

Section: Related Workmentioning

confidence: 99%

Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

Zhao

Yang

Zhang

et al. 2021

IET Computer Vision

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“…Many clustering methods based on deep neural networks, often referred to as deep clustering methods, have been developed. These existing deep clustering methods can mainly be divided into two categories, namely, the single-stage methods [2]- [15] and the two-stage methods [16], [17]. Specifically, the single-stage deep clustering methods generally seek to jointly learn feature representations and cluster assignments in an end-to-end framework.…”

Section: Introductionmentioning

confidence: 99%

“…Ji et al [10] developed the Invariant Information Clustering (IIC) method which learns a clustering function by maximizing the mutual information between the cluster assignments of data pairs. Besides these single-stage methods [2]- [15], some efforts in designing two-stage deep clustering methods have also been made [16], [17]. Van Gansbeke et al [16] proposed the Semantic Clustering by Adopting Nearest neighbors (SCAN) method, which conducts a pretext task of contrastive learning to mine the nearest neighbors in the first stage, and performs a further learning and clustering optimization based on the nearest neighbors in the next stage.…”

Section: Introductionmentioning

confidence: 99%

DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks

Dong¹,

Chen²,

Chen³

et al. 2022

Preprint

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“…The key of MVC is to find the consistency and complementary information among each view which is described by different aspects, which has been attracted enormous attention. Existing multi-view clustering approaches can be categorized into four categories according to the mechanisms and principles involved, namely, co-training, multikernel clustering, graph clustering and subspace clustering [5]- [9]. Co-training algorithms bootstrap the clustering results of different views by using the prior or learning knowledge from others [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

Zhang¹,

Wang²,

Tu³

et al. 2021

Preprint

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Multi-view clustering is an important yet challenging task in machine learning and data mining community. One popular strategy for multi-view clustering is matrix factorization which could explore useful feature representations at lowerdimensional space and therefore alleviate dimension curse. However, there are two major drawbacks in the existing work: i) most matrix factorization methods are limited to shadow depth, which leads to the inability to fully discover the rich hidden information of original data. Few deep matrix factorization methods provide a basis for the selection of the new representation's dimensions of different layers. ii) the majority of current approaches only concentrate on the view-shared information and ignore the specific local features in different views. To tackle the above issues, we propose a novel Multi-View Clustering method with Deep semi-NMF and Global Graph Refinement (MVC-DMF-GGR) in this paper. Firstly, we capture new representation matrices for each view by hierarchical decomposition, then learn a common graph by approximating a combination of graphs which are reconstructed from these new representations to refine the new representations in return. An alternate algorithm with proved convergence is then developed to solve the optimization problem and the results on six multi-view benchmarks demonstrate the effectiveness and superiority of our proposed algorithm.

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Deep Multi-View Subspace Clustering With Unified and Discriminative Learning

Cited by 114 publications

References 38 publications

Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks

Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

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