Learnable low-rank latent dictionary for subspace clustering
Abstract
Recently, Self-Expressive-based Subspace Clustering (SESC) has been widely applied in pattern clustering and machine learning as it aims to learn a representation that can faithfully reflect the correlation between data points. However, most existing SESC methods directly use the original data as the dictionary, which miss the intrinsic structure (e.g., low-rank and nonlinear) of the real-word data. To address this problem, we propose a novel...
Paper Details
Title
Learnable low-rank latent dictionary for subspace clustering
Published Date
Dec 1, 2021
Journal
Volume
120
Pages
108142 - 108142
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