Latent Multi-view Clustering Based Adaptive Graph Constraint
Authors: 
Wenbo Li, Bowen Shi, Daidai Zhu, Aihong YuanAuthors Info & Claims
Article No.: 16, Pages 1 - 7
Abstract
Graph-based multi-view clustering methods have demonstrated impressive outcomes in capturing the underlying manifold structure of data, leading to improved clustering performance. However, conventional graph-based methods overlook the significance of distinct features and rely solely on the learned graph based on raw features, potentially restraining their performance. To overcome these restrictions, we introduce an innovative method: Latent Multi-view Clustering Based Adaptive Graph Constraint(LMC_AGC). Our method incorporates manifold information from the original data and utilizes adaptive learning graphs to capture the relationships among samples. Specifically, the initial high-dimensional data is employed to reconstruct the latent representation matrix, and we construct the global similarity matrix through a linear amalgamation of affinity matrices across all perspectives. Subsequently, the technique of manifold regularization is utilized to improve the performance of the latent presentation model. Our method combines latent representation and adaptive graph learning within a unified framework optimized via an alternating iteration algorithm. Exhaustive experiments conducted on eight benchmark data sets affirm the performance of the proposed method. The source code is available at: https://github.com/Wenboli11/LMC_AGC.
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Published In
April 2024
381 pages
ISBN:9798400716393
DOI:10.1145/3661725
Copyright © 2024 ACM.
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Published: 20 June 2024
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- National College Students Innovation and Entrepreneurship Training Program
- Key Project of Shaanxi Provision-City Linkage
- National Natural Science Foundation of China
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CMLDS 2024
CMLDS 2024: 2024 International Conference on Computing, Machine Learning and Data Science
April 12 - 14, 2024
Singapore, Singapore
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