关键词: 子空间聚类, 多样性, 潜在嵌入空间, 希尔伯特施密特独立准则

Abstract:

Currently，multi?view subspace clustering is widely studied in fields of pattern recognition and machine learning. Previous multi?view clustering algorithms mostly partition the multi?view data in their original feature space，while the efficacy of which heavily and implicitly relies on the quality of the original feature presentation. In addition，different views contain specific information in a same object and how to use these views to recover latent diverse information is particularly important for clustering.To solve the above problems，this paper proposes a method named Diversity?induced Multi?view Clustering in Latent Embedded Space (DiMCLES)，which uses projection matrix on specific view to recover latent embedded space from multi?view data. This paper uses an emprical Hibert Schmidt Independent Criterion to constrain the projection matrix on specific view which considers the diverse information of multi?view data between different views. Latent embedded learning，diversity learning，global similarity learning and clustering indicator learning are integrated into a unified framework，and an alternating optimaization scheme is introduced for optimization. Experiments on several real?world multi?view datasets verifies the superiority of our approach.

Key words: subspace clustering, diversity, latent embedding space, Hilbert Schmidt Independence Criterion