Fast Robust Non-Negative Matrix Factorization for Large-Scale Human Action Data Clustering / 2104
De Wang, Feiping Nie, Heng Huang
Human action recognition is important in improving human life in various aspects. However, the outliers and noise in data often bother the clustering tasks. Therefore, there is a great need for the robust data clustering techniques. Nonnegative matrix factorization (NMF) and Nonnegative Matrix Tri-Factorization (NMTF) methods have been widely researched these years and applied to many data clustering applications. With the presence of outliers, most previous NMF/NMTF models fail to achieve the optimal clustering performance. To address this challenge, in this paper, we propose three new NMF and NMTF models which are robust to outliers. Efficient algorithms are derived, which converge much faster than previous NMF methods and as fast as K-means algorithm, and scalable to large-scale data sets. Experimental results on both synthetic and real world data sets show that our methods outperform other NMF and NMTF methods in most cases, and in the meanwhile, take much less computational time.