Feature Selection Embedded Subspace Clustering
Chong Peng, Zhao Kang, Ming Yang, Qiang Cheng · IEEE Signal Processing Letters · 2016
We propose a new subspace clustering method that integrates feature selection into subspace clustering. Rather than using all features to construct a low-rank representation of the data, we find such a representation using only relevant features, which helps in revealing more accurate data relationships. Two variants are proposed by using both convex and nonconvex rank approximations. Extensive experimental results confirm the effectiveness of the proposed method and models.