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On the relation between K-means and PLSA

Chaudhuri, Arghya Roy and Murty, Narasimha M (2012) On the relation between K-means and PLSA. In: 2012 21st International Conference on Pattern Recognition (ICPR), 11-15 Nov. 2012, Tsukuba, Japan.

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Non-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. It can be viewed as a generalization of the K-means clustering, Expectation Maximization based clustering and aspect modeling by Probabilistic Latent Semantic Analysis (PLSA). Specifically PLSA is related to NMF with KL-divergence objective function. Further it is shown that K-means clustering is a special case of NMF with matrix L2 norm based error function. In this paper our objective is to analyze the relation between K-means clustering and PLSA by examining the KL-divergence function and matrix L2 norm based error function.

Item Type: Conference Paper
Publisher: IEEE
Additional Information: Copyright of this article belongs to IEEE.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 02 Jul 2013 08:33
Last Modified: 02 Jul 2013 08:33
URI: http://eprints.iisc.ac.in/id/eprint/46623

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