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- W4387076124 abstract "Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC." @default.
- W4387076124 created "2023-09-27" @default.
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- W4387076124 date "2023-09-25" @default.
- W4387076124 modified "2023-10-03" @default.
- W4387076124 title "A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective" @default.
- W4387076124 doi "https://doi.org/10.48550/arxiv.2309.13989" @default.
- W4387076124 hasPublicationYear "2023" @default.
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