Progressive Deep Subspace Clustering based on Sample Reliability

Hang Gao, Yunshan Li, Cheng Liu

Abstract: Deep subspace clustering methods have attracted extensive attention due to the great improvement in both representation ability and precision of non-linear data. However, the rich information which is contained in the self-expression matrix is unexplored since existing approaches use the self-expression matrix only as a tool for learning inter-sample relationships and clustering. In addition, such models treat outlier and noise points equally with other points, which inevitably degrades the clustering performance. To overcome these issues, we develop a progressive deep subspace clustering approach by extracting delayed fitting probabilities from the module and then use the probabilities to defer the fitting of unreliable points. Specifically, we calculate the probabilities that each sample lies in each subspace based on the results of the self-expression matrix and spectral clustering, and then estimate the reliability of the cluster assignment of each sample as delayed fitting probability to reweight the loss of each sample. Experiments on five benchmark datasets validate the effectiveness of the proposed method.