Abstract Details
Activity Number:
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120
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 5, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #309738 |
Title:
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A Unified Statistical Approach to Non-Negative Matrix Factorization and Probabilistic Latent Semantic Indexing
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Author(s):
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Karthik Devarajan*+ and Guoli Wang and Nader Ebrahimi
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Companies:
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Fox Chase Cancer Center and SRA International Inc. and Northern Illinois University
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Keywords:
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Renyi divergence ;
non-negative matrix factorization ;
probabilistic latent semantic indexing ;
Poisson likelihood ;
biomedical informatics ;
text mining
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Abstract:
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Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix into the product of two nonnegative matrices. NMF has been shown to have a unique parts-based, sparse representation of the data. NMF has been successfully applied in diverse areas such as natural language processing, information retrieval, signal processing and computational biology for analyzing large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely probabilistic latent semantic indexing (PLSI), for analyzing co-occurrence count data arising in natural language processing. In this talk, we describe a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices related to the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods by generalizing the relationship between them. We demonstrate the applicability of our approach using document clustering and text mining data arising in biomedical informatics.
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Authors who are presenting talks have a * after their name.
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