Name
Abstract | ||
|---|---|---|
A new matrix factorization algorithm which combines two recently proposed nonnegative learning techniques is presented. Our new algorithm, α -PNMF, inherits the advantages of Projective Nonnegative Matrix Factorization (PNMF) for learning a highly orthogonal factor matrix. When the Kullback-Leibler (KL) divergence is generalized to α -divergence, it gives our method more flexibility in approximation. We provide multiplicative update rules for α -PNMF and present their convergence proof. The resulting algorithm is empirically verified to give a good solution by using a variety of real-world datasets. For feature extraction, α -PNMF is able to learn highly sparse and localized part-based representations of facial images. For clustering, the new method is also advantageous over Nonnegative Matrix Factorization with α -divergence and ordinary PNMF in terms of higher purity and smaller entropy. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04274-4_3 | ICANN (1) |
Keywords | Field | DocType |
nonnegative matrix factorization,convergence proof,orthogonal factor matrix,resulting algorithm,facial image,new method,new algorithm,ordinary pnmf,new matrix factorization algorithm,projective nonnegative matrix factorization,kullback leibler,feature extraction,matrix factorization | Convergence (routing),Combinatorics,Nonnegative matrix,Multiplicative function,Matrix (mathematics),Matrix decomposition,Feature extraction,Non-negative matrix factorization,Cluster analysis,Mathematics | Conference |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhirong Yang | 1 | 289 | 17.27 |
| Erkki Oja | 2 | 6701 | 797.08 |