Discriminative Joint Non-negative Matrix Factorization for Human Action Classification

This paper describes a supervised classification approach based on non-negative matrix factorization (NMF). Our classification framework builds on the recent expansions of non-negative matrix factorization to multiview learning, where the primary dataset benefits from auxiliary information for obtaining shared and meaningful spaces. For discrimination, we utilize data categories in a supervised manner as an auxiliary source of information in order to learn co-occurrences through a common set of basis vectors. We demonstrate the efficiency of our algorithm in integrating various image modalities for enhancing the overall classification accuracy over different benchmark datasets. Our evaluation considers two challenging image datasets of human action recognition. We show that our algorithm achieves superior results over state-of-the-art in terms of efficiency and overall classification accuracy.