Sequential Subspace Clustering via Temporal Smoothness

Haijun Liu, Jian Cheng, Feng Wang

Abstract: This paper develops a novel sequential subspace clustering method for sequential data. Inspired by state-of-the-art methods ordered subspace clustering (OSC) and temporal subspace clustering (TSC), we design a novel local temporal regularization term based on the concept of temporal predictability, which is measured by short-term variance against long-term variance, to recover the temporal smoothness relationships in sequential data. To solve the bi-convex objective function, a simple and efficient optimization algorithm based on the alternate convex search (ACS) method is devised to jointly learn the codings matrix and dictionary. Extensive experimental results and comparisons with state-of-the-art methods on gesture and face datasets demonstrate the effectiveness of the proposed temporal smoothness sequential subspace clustering method for sequential data.