Self Supervised Correlation-based Permutations for Multi-View Clustering

• URL: http://arxiv.org/abs/2402.16383v2
• Date: Tue, 20 May 2025 14:02:03 GMT
• Title: Self Supervised Correlation-based Permutations for Multi-View Clustering
• Authors: Ran Eisenberg, Jonathan Svirsky, Ofir Lindenbaum,
• Abstract summary: We propose an end-to-end deep learning-based multi-view clustering framework for general data types.<n>Our approach involves generating meaningful fused representations using a novel permutation-based canonical correlation objective.
• Score: 7.093692674858257
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Combining data from different sources can improve data analysis tasks such as clustering. However, most of the current multi-view clustering methods are limited to specific domains or rely on a suboptimal and computationally intensive two-stage process of representation learning and clustering. We propose an end-to-end deep learning-based multi-view clustering framework for general data types (such as images and tables). Our approach involves generating meaningful fused representations using a novel permutation-based canonical correlation objective. We provide a theoretical analysis showing how the learned embeddings approximate those obtained by supervised linear discriminant analysis (LDA). Cluster assignments are learned by identifying consistent pseudo-labels across multiple views. Additionally, we establish a theoretical bound on the error caused by incorrect pseudo-labels in the unsupervised representations compared to LDA. Extensive experiments on ten multi-view clustering benchmark datasets provide empirical evidence for the effectiveness of the proposed model.

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