Sciweavers

Share
ICPR
2008
IEEE

Incremental clustering via nonnegative matrix factorization

10 years 4 days ago
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.
Serhat Selcuk Bucak, Bilge Günsel
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Serhat Selcuk Bucak, Bilge Günsel
Comments (0)
books