Structural constraint deep matrix factorization for sequential data clustering

Abstract

Nonnegative matrix factorization (NMF) plays a significant role of finding parts-based representations of nonnegative data that is widely used in data analysis applications. However, sequential data (e.g., video scene, human action) with ordered structures usually share obvious similar features between neighboring data points unless a sudden change occurs, it is important to exploit temporal information for sequential data representation. However, this remains a challenging problem for NMF-based methods, which are unsuitable for the analysis of such data. In this work, we propose structural constraint deep matrix factorization (SC-MF), which captures the ordered structure information into the deep matrix factorization process to improve data representation. With a novel neighbor penalty term in each layer process, SC-MF enforces the similarity of neighboring data in the final layer. The appropriate iterative updating algorithm is derived to solve SC-MF’s objective function. The proofs of the convergence and complexity of the SC-MF are also presented. Experimental results on several real sequential datasets for face clustering, video scene segmentation, and action segmentation tasks demonstrate the effectiveness of our approach.