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Towards a unified framework for graph-based multi-view clustering

NEURAL NETWORKS 2024年 173卷 106197页

作者： Dornaika, F. El Hajjar, S. Univ Basque Country UPV EHU San Sebastian Spain IKERBASQUE Basque Fdn Sci Bilbao Spain Ho Chi Minh City Open Univ Ho Chi Minh City Vietnam

Recently, clustering data collected from various sources has become a hot topic in real-world applications. The most common methods for multi-view clustering can be divided into several categories: Spectral clustering algorithms, subspace multi-view clustering algorithms, matrix factorization approaches, and kernel methods. Despite the high performance of these methods, they directly fuse all similarity matrices of all views and separate the affinity learning process from the multiview clustering process. The performance of these algorithms can be affected by noisy affinity matrices. To overcome this drawback, this paper presents a novel method called One Step multi-view clustering via Consensus graph Learning and Nonnegative Embedding (OSMGNE). Instead of directly merging the similarity matrices of different views, which may contain noise, a step of learning a consensus similarity matrix is performed. This step forces the similarity matrices of different views to be too similar, which eliminates the problem of noisy data. Moreover, the use of the nonnegative embedding matrix (soft cluster assignment matrix makes it possible to directly obtain the final clustering result without any extra step. The proposed method can solve five subtasks simultaneously. It jointly estimates the similarity matrix of all views, the similarity matrix of each view, the corresponding spectral projection matrix, the unified clustering indicator matrix, and automatically gives the weight of each view without the use of hyper-parameters. In addition, another version of our method is also studied in this paper. This method differs from the first one by using a consensus spectral projection matrix and a consensus Laplacian matrix over all views. An iterative algorithm is proposed to solve the optimization problem of these two methods. The two proposed methods are tested on several real datasets, which prove their superiority.

关键词： graph-based multi-view clustering Unified and consensus graph learning Data fusion Spectral embedding Soft cluster assignments

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An End-to-End Approach for graph-based multi-view Data clustering

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IEEE TRANSACTIONS ON BIG DATA 2024年第5期10卷 644-654页

作者： Dornaika, Fadi El Hajjar, Sally Univ Basque Country UPV EHU San Sebastian 20018 Spain Ho Chi Minh City Open Univ Ho Chi Minh City 700000 Vietnam

clustering data from different sources or views is a key challenge in real-world applications. While traditional graph-based methods are effective at capturing data structures, they often require separate steps to estimate graphs of views or a consensus graph from the raw data. This reliance on intermediate steps can make these clustering methods susceptible to noisy graphs, which affects the overall performance of clustering. In response to this limitation, and with an emphasis on advocating end-to-end solutions for multi-view clustering, two comprehensive approaches are presented in this paper. Each approach starts from either the raw data or its kernelized features. The first proposal introduces a unified objective function that enables the simultaneous recovery of the graph for each view, the unified graph, the spectral projection matrices for all views, the soft cluster assignments, and the scores assigned to each view. The second proposal uses a global criterion that integrates regularization and constraints for the soft cluster assignment matrix based on the consensus graph matrix and the consensus data representation. Both proposed methods enable direct and straightforward clustering of the data without the need for additional steps. Extensive tests with various real-world image and text datasets confirm the superior performance of the two proposed methods.

关键词： clustering algorithms Vectors Big Data Smoothing methods Proposals Linear programming Convolution graph-based multi-view clustering unified graph learning data fusion spectral embedding soft cluster assignments

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Unified multi-view Data clustering: Simultaneous Learning of Consensus Coefficient Matrix and Similarity graph

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COGNITIVE COMPUTATION 2025年第1期17卷 1-16页

作者： Dornaika, F. El Hajjar, S. Charafeddine, J. Barrena, N. Univ Basque Country UPV EHU San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain Leonard Vinci Pole Univ Res Ctr F-92916 Paris France

Integrating data from multiple sources or views has become increasingly common in data analysis, particularly in fields like healthcare, finance, and social sciences. However, clustering such multi-view data poses unique challenges due to the heterogeneity and complexity of the data sources. Traditional clustering methods are often unable to effectively leverage the information from different views, leading to suboptimal clustering results. To address this challenge, multi-view clustering techniques have been developed, aiming to integrate information from multiple views to improve clustering performance. These techniques typically involve learning a similarity matrix for each view and then combining these matrices to form a consensus similarity matrix, which is subsequently used for clustering. However, existing approaches often suffer from limitations such as the need for manual tuning of parameters and the inability to effectively capture the underlying structure of the data. In this paper, we propose a novel approach for multi-view clustering that addresses these limitations by jointly learning the consensus coefficient matrix and similarity graph. Unlike existing methods that follow a sequential approach of first learning the coefficient matrix and then constructing the similarity graph, our approach simultaneously learns both matrices, ensuring a more regularized consensus graph. Additionally, our method automatically adjusts the weight of each view, eliminating the need for manual parameter tuning. Our approach involves several key steps. First, we formulate an optimization problem that jointly optimizes the consensus coefficient matrix, unified spectral projection matrix, coefficient matrix, and soft cluster assignment matrix. We then propose an efficient algorithm to solve this optimization problem, which involves iteratively updating the matrices until convergence. To learn the consensus coefficient matrix and similarity graph, we leverage techniques from

关键词： graph-based multi-view clustering Unified and consensus graph learning Data fusion Spectral embedding Soft cluster assignments

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From multi-view data features to clusters: a unified approach

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ARTIFICIAL INTELLIGENCE REview 2023年第SUPPL3期56卷 S3821-S3852页

作者： Dornaika, Fadi Ho Chi Minh City Open Univ 97 Vo Van TanWard Vo Thi SauDist 3 Ho Chi Minh City 70000 Vietnam

clustering data from multiple sources or views has become an important issue in real-world applications. graph-based methods take advantage of graphs that encode the local and global structure of the data. Although graph-based methods provide good clustering performance, they need to estimate the view graphs or the consensus graph from the raw data in a separate step. Their performance may be affected by the noisy graphs. To overcome this limitation and promote end-to-end multi-view clustering solutions, this paper presents two end-to-end multi-view clustering solutions starting from the data or their kernel representations. The first proposed solution is based on a single objective function that allows the joint estimation of the graph of each view, the consensus graph, the spectral projection matrices for all views, the soft clustering assignments, and the weights of each view. The second solution uses a different objective function where the links and constraints for the soft clustering assignment matrix use the consensus graph matrix and the consensus spectral projection matrix. Both methods enforce the mutual similarity of each graph and provide a direct clustering result without any additional step. The two proposed methods are tested on several real image and text datasets, which prove their superiority.

关键词： graph-based multi-view clustering Consensus graph learning Fusion Spectral representation graph convolution Cluster assignments

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