Auto-weighted low-rank representation for clustering
This is an incremental improvement for unsupervised clustering tasks, addressing the challenge of learning more discriminative similarity graphs.
The paper tackles the problem of constructing a similarity graph for clustering by proposing Auto-weighted Low-Rank Representation (ALRR), which enhances discriminability through multi-subspace structure and salient feature extraction, resulting in performance improvements of 1.8% to 10.8% over state-of-the-art methods.
In this paper, a novel unsupervised low-rank representation model, i.e., Auto-weighted Low-Rank Representation (ALRR), is proposed to construct a more favorable similarity graph (SG) for clustering. In particular, ALRR enhances the discriminability of SG by capturing the multi-subspace structure and extracting the salient features simultaneously. Specifically, an auto-weighted penalty is introduced to learn a similarity graph by highlighting the effective features, and meanwhile, overshadowing the disturbed features. Consequently, ALRR obtains a similarity graph that can preserve the intrinsic geometrical structures within the data by enforcing a smaller similarity on two dissimilar samples. Moreover, we employ a block-diagonal regularizer to guarantee the learned graph contains $k$ diagonal blocks. This can facilitate a more discriminative representation learning for clustering tasks. Extensive experimental results on synthetic and real databases demonstrate the superiority of ALRR over other state-of-the-art methods with a margin of 1.8\%$\sim$10.8\%.