Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
نویسندگان
چکیده
منابع مشابه
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
MOTIVATION Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approx...
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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 ...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2007
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btm134