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Statistics > Machine Learning

arXiv:1401.1842 (stat)
[Submitted on 8 Jan 2014]

Title:Robust Large Scale Non-negative Matrix Factorization using Proximal Point Algorithm

Authors:Jason Gejie Liu, Shuchin Aeron
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Abstract:A robust algorithm for non-negative matrix factorization (NMF) is presented in this paper with the purpose of dealing with large-scale data, where the separability assumption is satisfied. In particular, we modify the Linear Programming (LP) algorithm of [9] by introducing a reduced set of constraints for exact NMF. In contrast to the previous approaches, the proposed algorithm does not require the knowledge of factorization rank (extreme rays [3] or topics [7]). Furthermore, motivated by a similar problem arising in the context of metabolic network analysis [13], we consider an entirely different regime where the number of extreme rays or topics can be much larger than the dimension of the data vectors. The performance of the algorithm for different synthetic data sets are provided.
Comments: Appeared in IEEE GlobalSIP, 2013, TX, Austin
Subjects: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG); Numerical Analysis (math.NA)
Cite as: arXiv:1401.1842 [stat.ML]
  (or arXiv:1401.1842v1 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.1401.1842

Submission history

From: Shuchin Aeron [view email]
[v1] Wed, 8 Jan 2014 21:39:03 UTC (244 KB)
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