کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
531542 869853 2008 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
SVD based initialization: A head start for nonnegative matrix factorization
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
SVD based initialization: A head start for nonnegative matrix factorization
چکیده انگلیسی

We describe Nonnegative Double Singular Value Decomposition (NNDSVD), a new method designed to enhance the initialization stage of nonnegative matrix factorization (NMF). NNDSVD can readily be combined with existing NMF algorithms. The basic algorithm contains no randomization and is based on two SVD processes, one approximating the data matrix, the other approximating positive sections of the resulting partial SVD factors utilizing an algebraic property of unit rank matrices. Simple practical variants for NMF with dense factors are described. NNDSVD is also well suited to initialize NMF algorithms with sparse factors. Many numerical examples suggest that NNDSVD leads to rapid reduction of the approximation error of many NMF algorithms.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 41, Issue 4, April 2008, Pages 1350–1362
نویسندگان
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