When to Learn What: Deep Cognitive Subspace Clustering

Abstract

Subspace clustering aims at clustering data points drawn from a union of low-dimensional subspaces. Recently deep neural networks are introduced into this problem to improve both representation ability and precision for non-linear data. However, such models are sensitive to noise and outliers, since both difficult and easy samples are treated equally. On the contrary, in the human cognitive process, individuals tend to follow a learning paradigm from easy to hard and less to more. In other words, human beings always learn from simple concepts, then absorb more complicated ones gradually. Inspired by such learning scheme, in this paper, we propose a robust deep subspace clustering framework based on the principle of human cognitive process. Specifically, we measure the easinesses of samples dynamically so that our proposed method could gradually utilize instances from easy to more complex ones in a robust way. Meanwhile, a promising solution is designed to update the weights and parameters using an alternative optimization strategy, followed by a theoretical analysis to demonstrated the rationality of the proposed method. Experimental results on three popular benchmark datasets demonstrate the validity of the proposed method.