Incremental clustering via nonnegative matrix factorization

Nonnegative matrix factorization (NMF) has been shown to be an efficient clustering tool. However, NMF`s batch nature necessitates recomputation of whole basis set for new samples. Although NMF is a powerful content representation tool, this limits the use of NMF in online processing of large data sets. Another problem with NMF, like other partitional methods, is determining the actual number of clusters. Deciding the rank of the factorization is also critical since it has a significant effect on clustering performance. This paper introduces an NMF based incremental clustering algorithm which allows increasing number of clusters adaptively thus eliminates optimal rank selection problem. Test results obtained on large video data sets demonstrate that the proposed clustering scheme is capable of labeling linearly separable data as well as non-separable samples with a small false positive ratio.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Serhat Selcuk Bucak, Bilge Günsel
• Keywords: Computer Vision, Data Sets, Efficient Clustering Tool, ICPR 2008, Nonnegative Matrix Factorization