Novel Algorithm for Non-Negative Matrix Factorization

Tran Dang Hien (), Do Van Tuan (), Pham Van At () and Le Hung Son ()
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Tran Dang Hien: Vietnam National University, Hanoi, Vietnam
Do Van Tuan: Hanoi College of Commerce and Tourism, Hanoi, Vietnam
Pham Van At: Hanoi University of Communications and Transport, Hanoi, Vietnam
Le Hung Son: Hanoi University of Science and Technology, Hanoi, Vietnam

New Mathematics and Natural Computation (NMNC), 2015, vol. 11, issue 02, 121-133

Abstract: Non-negative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in data analysis. Mathematically, NMF can be formulated as a minimization problem with non-negative constraints. This problem attracts much attention from researchers for theoretical reasons and for potential applications. Currently, the most popular approach to solve NMF is the multiplicative update algorithm proposed by Lee and Seung. In this paper, we propose an additive update algorithm that has a faster computational speed than Lee and Seung's multiplicative update algorithm.

Keywords: NMF; non-negative matrix factorization; KKT; Krush–Kuhn–Tucker optimal condition; the stationarity point; updating an element of matrix; updating matrices (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S1793005715400013

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