Name
Abstract | ||
|---|---|---|
Projective Nonnegative Matrix Factorization (PNMF) has demonstrated advantages in both sparse feature extraction and clustering. However, PNMF requires users to specify the column rank of the approximative projection matrix, the value of which is unknown before-hand. In this paper, we propose a method called ARDPNMF to automatically determine the column rank in PNMF. Our method is based on automatic relevance determination (ARD) with Jeffrey's prior. After deriving the multiplicative update rule using the expectation-maximization technique for ARDPNMF, we test it on various synthetic and real-world datasets for feature extraction and clustering applications to show the effectiveness of our algorithm. For FERET faces and the Swimmer dataset, interpretable number of features are obtained correctly via our algorithm. Several UCI datasets for clustering are also tested, in which we find that ARDPNMF can estimate the number of clusters quite accurately with low deviation and good cluster purity. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15995-4_64 | LVA/ICA |
Keywords | Field | DocType |
clustering application,real-world datasets,column rank,approximative projection matrix,swimmer dataset,feature extraction,uci datasets,sparse feature extraction,interpretable number,automatic rank determination,projective nonnegative matrix factorization,expectation maximization,nonnegative matrix factorization | Pattern recognition,Multiplicative function,Nonnegative matrix,Projection (linear algebra),Feature extraction,Non-negative matrix factorization,Artificial intelligence,Relevance vector machine,Cluster analysis,Mathematics,Projective test | Conference |
Volume | ISSN | ISBN |
6365 | 0302-9743 | 3-642-15994-X |
Citations | PageRank | References |
4 | 0.47 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhirong Yang | 1 | 289 | 17.27 |
| Zhanxing Zhu | 2 | 199 | 29.61 |
| Erkki Oja | 3 | 6701 | 797.08 |