Essential Low-Rank Sample Learning for Group-Aware Subspace Clustering

In this letter, we proposea novel subspace clustering method, named Es
$^{3}$
SC, that learns essential samples for low-dimensional representation construction. The essential samples are expected to retain key features and better estimate the example-wise similarities, which are more geared to seeking the representation matrix (RM). Moreover, the RM is enforced to have block-diagonal structural property, which directly reveals grouping structure of the data and is essentially desired by clustering application. Experimental results show that the Es
$^{3}$
SC is effective in both clustering and essential feature recovery.