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Relation between PLSA and NMF and implications
Abstract
Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) have been successfully applied to a number of text analysis tasks such as document clustering. Despite their different inspirations, both methods are instances of multinomial PCA [1]. We further explore this relationship and first show that PLSA solves the problem of NMF with KL divergence, and then explore the implications of this relationship.
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McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
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Anderson, A., Douglas, P. K., Kerr, W. T., Haynes, V. S., Yuille, A. L., Xie, J., Wu, Y. N., Brown, J. A., & Cohen, M. S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.015 · Cited by 136
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Metadata
- Authors
- Gaussier, E., & Goutte, C.
- Venue
- Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2005-08-15
- Pages
- 601-602
- OpenAlex
- W2028080169
- Cited by
- 264
- References
- 1
- FWCI
- 7.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Gaussier, E., & Goutte, C. (2005). Relation between PLSA and NMF and implications. Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1076034.1076148
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