Nonnegative Matrix Factorization

Ke-Lin Du () and M. N. S. Swamy
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Ke-Lin Du: Concordia University, Department of Electrical and Computer Engineering
M. N. S. Swamy: Concordia University, Department of Electrical and Computer Engineering

Chapter Chapter 14 in Neural Networks and Statistical Learning, 2019, pp 427-445 from Springer

Abstract: Abstract Low-rank matrix factorization or factor analysis is an important task that is helpful in the analysis of high-dimensional real-world data such as dimension reduction, data compression, feature extraction, and information retrieval. Nonnegative matrix factorization is a special low-rank factorization technique for nonnegative data. This chapter is dedicated to nonnegative matrix factorization. Other matrix decomposition methods, such as Nystrom method and CUR matrix decomposition, are also introduced in this chapter.

Date: 2019
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DOI: 10.1007/978-1-4471-7452-3_14

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