Email Surveillance Using Non-negative Matrix Factorization
Computational & Mathematical Organization Theory
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
IEEE Transactions on Neural Networks
Musical Genre Classification Using Nonnegative Matrix Factorization-Based Features
IEEE Transactions on Audio, Speech, and Language Processing
Global Convergence of Decomposition Learning Methods for Support Vector Machines
IEEE Transactions on Neural Networks
On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization
IEEE Transactions on Neural Networks
Global convergence of modified multiplicative updates for nonnegative matrix factorization
Computational Optimization and Applications
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Nonnegative matrix factorization (NMF) is to approximate a given large nonnegative matrix by the product of two small nonnegative matrices. Although the multiplicative update algorithm is widely used as an efficient computation method for NMF, it has a serious drawback that the update formulas are not well-defined because they are expressed in the form of a fraction. Furthermore, due to this drawback, the global convergence of the algorithm has not been guaranteed. In this paper, we consider NMF in which the approximation error is measured by the Euclidean distance between two matrices. We propose a modified multiplicative update algorithm in order to overcome the drawback of the original version and prove its global convergence.