Cross-View Approximation on Grassmann Manifold for Multiview Clustering

Yidan Ma, Xinjie Shen, Danyang Wu, Jianfu Cao, Feiping Nie

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

In existing multiview clustering research, the comprehensive learning from multiview graph and feature spaces simultaneously remains insufficient when achieving a consistent clustering structure. In addition, a postprocessing step is often required. In light of these considerations, a cross-view approximation on Grassman manifold (CAGM) model is proposed to address inconsistencies within multiview adjacency matrices, feature matrices, and cross-view combinations from the two sources. The model uses a ratio-formed objective function, enabling parameter-free bidirectional fusion. Furthermore, the CAGM model incorporates a paired encoding mechanism to generate low-dimensional and orthogonal cross-view embeddings. Through the approximation of two measurable subspaces on the Grassmann manifold, the direct acquisition of the indicator matrix is realized. Furthermore, an effective optimization algorithm corresponding to the CAGM model is derived. Comprehensive experiments on four real-world datasets are conducted to substantiate the effectiveness of our proposed method.

源语言英语
页(从-至)7772-7777
页数6
期刊IEEE Transactions on Neural Networks and Learning Systems
36
4
DOI
出版状态已出版 - 2025

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