Potluru, Vamsi K. and Plis, Sergey M. and Pearlmutter, Barak A. and Calhoun, Vince D. and Hayes, Thoms P. (2011) Sequential Sparse NMF. In: 7th Student Conference, 2011.

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Abstract

Nonnegative Matrix Factorization (NMF) is a standard tool for data analysis. An important variant is the Sparse NMF problem. A natural measure of sparsity is the L₀ norm, however its optimization is NP-hard. Here, we consider a sparsity measure linear in the ratio of the L₁ and L₂ norms, and propose an efficient algorithm to handle the norm constraints which arise when optimizing this measure. Although algorithms for solving these are available, they are typically inefficient. We present experimental evidence that our new algorithm performs an order of magnitude faster compared to the previous state-of-the-art.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Sparse NMF; Nonnegative Matrix Factorization; data analysis;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	10242
Depositing User:	Barak Pearlmutter
Date Deposited:	27 Nov 2018 15:37
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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