On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing |
| |
Authors: | Chris Ding Wei Peng |
| |
Affiliation: | a Department of CSE, University of Texas at Arlington, Arlington, TX 76019, United States b School of Computer Science, Florida International University, Miami, FL 33199, United States |
| |
Abstract: | Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been successfully applied to document clustering recently. In this paper, we show that PLSI and NMF (with the I-divergence objective function) optimize the same objective function, although PLSI and NMF are different algorithms as verified by experiments. This provides a theoretical basis for a new hybrid method that runs PLSI and NMF alternatively, each jumping out of the local minima of the other method successively, thus achieving a better final solution. Extensive experiments on five real-life datasets show relations between NMF and PLSI, and indicate that the hybrid method leads to significant improvements over NMF-only or PLSI-only methods. We also show that at first-order approximation, NMF is identical to the χ2-statistic. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|