کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
381682 1437508 2007 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Initialization enhancer for non-negative matrix factorization
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Initialization enhancer for non-negative matrix factorization
چکیده انگلیسی

Non-negative matrix factorization (NMF), proposed recently by Lee and Seung, has been applied to many areas such as dimensionality reduction, image classification image compression, and so on. Based on traditional NMF, researchers have put forward several new algorithms to improve its performance. However, particular emphasis has to be placed on the initialization of NMF because of its local convergence, although it is usually ignored in many documents. In this paper, we explore three initialization methods based on principal component analysis (PCA), fuzzy clustering and Gabor wavelets either for the consideration of computational complexity or the preservation of structure. In addition, the three methods develop an efficient way of selecting the rank of the NMF in low-dimensional space.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 20, Issue 1, February 2007, Pages 101–110
نویسندگان
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