Tensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data

Li, Zhenglai; Tang, Chang; Liu, Xinwang; Zheng, Xiao; Zhang, Wei; Zhu, En

doi:10.1109/icme51207.2021.9428106

Cited by 15 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sparsity of W cannot be guaranteed even though W (v) is sparse in each view. Moreover, the sparsity of the result of Algorithm 2 is determined using (20). The parameter α is not known, and µ k always increases during the iterations of Algorithm 2.…”

Section: Sparsity Augmented Fusion Of Sparse Representationsmentioning

confidence: 99%

“…Recently, some IMVC methods have been proposed to alleviate the above problem. From the perspective of clustering techniques, existing IMVC methods are mainly divided into five categories, subspace learning-based methods [41], [43], nonnegative matrix factorization (NMF)-based methods [15], [16], graph learning-based methods [20], [39], multiple kernelbased methods [24], [23] and deep learning-based methods [38], [42], [21], [44], [46]. Wen et al present an extension of a low-rank representation (LRR) model that incorporates feature space-based missing-view inferring and manifold space-based similarity graph learning [41].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Augmented Sparse Representation for Incomplete Multiview Clustering

Chen

Yang

Peng

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Incomplete multiview data are collected from multiple sources or characterized by multiple modalities, where the features of some samples or some views may be missing. Incomplete multiview clustering aims to partition the data into different groups by taking full advantage of the complementary information from multiple incomplete views. Most existing methods based on matrix factorization or subspace learning attempt to recover the missing views or perform imputation of the missing features to improve clustering performance. However, this problem is intractable due to a lack of prior knowledge, e.g., label information or data distribution, especially when the missing views or features are completely damaged. In this paper, we proposed an augmented sparse representation (ASR) method for incomplete multiview clustering. We first introduce a discriminative sparse representation learning (DSRL) model, which learns the sparse representations of multiple views as applied to measure the similarity of the existing features. The DSRL model explores complementary and consistent information by integrating the sparse regularization item and a consensus regularization item, respectively. Simultaneously, it learns a discriminative dictionary from the original samples. The sparsity constrained optimization problem in the DSRL model can be efficiently solved by the alternating direction method of multipliers. Then, we present a similarity fusion scheme, namely, a sparsity augmented fusion of sparse representations, to obtain a sparsity augmented similarity matrix across different views for spectral clustering. Experimental results on several datasets demonstrate the effectiveness of the proposed ASR method for incomplete multiview clustering.

show abstract

Section: Sparsity Augmented Fusion Of Sparse Representationsmentioning

confidence: 99%

mentioning

confidence: 99%

Augmented Sparse Representation for Incomplete Multiview Clustering

Chen

Yang

Peng

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…It obtains the graph structure 𝑆 𝑣 for each view by (3), and the graph structures of different views will be integrated into the global graph A: However, it is difficult for k-means to achieve the desired classification. Equation (3) replaces the traditional optimization method when using the rank constraint of the Laplacian matrix in the optimization problem, and the performance is improved.…”

Section: Graph Learningmentioning

confidence: 99%

“…Incomplete multi-view clustering algorithms are gradually applied to practical problems, making incomplete multi-view clustering one of the hottest research directions in this area. Traditional IMVC methods can be roughly divided into four categories, i.e., non-negative matrix factorizationbased methods [2,3,4], multi-view subspace clustering [5], kernel learning-based methods [6,7], and graph learning-based methods [8,9]. Among them, the method based on non-negative matrix factorization learns a low-dimensional consistent representation through all view information for clustering.…”

Section: Introductionmentioning

confidence: 99%

Deep multi-view graph clustering with incomplete views

Chang,

Zhu,

Miao

2024

Preprint

View full text Add to dashboard Cite

Deep multi-view graph clustering has made good progress in solving large-scale problems. However, existing deep multi-view graph clustering methods suffer from the following issues: (1) How to combine data processing with multi-view clustering in deep learning, (2) How to learn the local and global information of the graph in deep learning. To this end, a novel method, called deep multi-view graph clustering with the incomplete view (DMVGC-IV), is proposed in this paper, which successfully solves the above two difficulties. Specifically, deep metric learning networks are employed on multiple views to obtain the graph structure. It approximately maintains the semantic distance of data points in the subspace. Then, DMVGC-IV combines global and local structures with a graph-fusion layer. By integrating autoencoder’s reconstruction and multi-view graph learning into a unified framework, our model can jointly optimize the cluster label assignments and embeddings suitable for graph clustering. Experiments use five commonly used multi-view data sets and compare them with five advanced multi-view clustering methods to verify the effectiveness of the proposed method.

show abstract

“…2) The learning of each view is separate and does not take inter-view similarity structure, which greatly reduces the advantages of multi-view data. Based on this observation, a series of tensor-based multi-view subspace representation methods have emerged to infer missing samples while clustering [8][9][10][11][12], which all consider tensor singular value decomposition (t-SVD) [13] to explore the high-order correlations among different views.…”

Section: Introductionmentioning

confidence: 99%

Feature Space Recovery for Incomplete Multi-View Clustering

Zhang

Zhu

Comon

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Incomplete multi-view clustering (IMVC), based on imputation and clustering unification, has received wide attention due to its ability to exploit hidden information from missing views. However, current methods mainly consider inter/intraview correlations, ignoring the structural information of sample features within views. In this paper, we propose a feature space recovery based IMVC method, where low-rank feature space recovery and consensus representation learning of inter/intra-views are considered into a unified framework. Moreover, low-rank tensor ring approximation is used to capture the correlations of self-representation tensor. In an iterative way, the learned inter/intra-view correlations will guide the recovery of missing features, while the explored low-rank information from feature spaces will in turn facilitate self-representation learning, eventually achieving outstanding clustering performance. Experimental results show our method has a very significant improvement over known state-of-the-art algorithms in terms of ACC, NMI and Purity.

show abstract

Tensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data

Cited by 15 publications

References 12 publications

Augmented Sparse Representation for Incomplete Multiview Clustering

Augmented Sparse Representation for Incomplete Multiview Clustering

Deep multi-view graph clustering with incomplete views

Feature Space Recovery for Incomplete Multi-View Clustering

Contact Info

Product

Resources

About