Title:
Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares
Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares
Author(s)
Kim, Hyunsoo
Park, Haesun
Park, Haesun
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Abstract
Many practical pattern recognition problems require non-negativity constraints.
For example, pixels in digital images and chemical concentrations in bioinformatics
are non-negative. Non-negative matrix factorization (NMF) is a useful technique
in approximating these high dimensional data. Sparse NMFs are also useful
when we need to control the degree of sparseness in non-negative basis vectors
or non-negative lower-dimensional representations. In this paper, we introduce
novel sparse NMFs via alternating non-negativity-constrained least squares. We
applied one of the proposed sparse NMFs to cancer class discovery and gene expression
data analysis. Our experimental results illustrate that our proposed method
achieves better clustering performance than NMF based on multiplicative update
rules and sparse NMFs based on the gradient descent method.
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Date Issued
2006
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Text
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Technical Report