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Improving non-negative matrix factorizations through structured initialization
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DOI：10.1016/j.patcog.2004.02.013.png)
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摘要
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In this paper we explore a recent iterative compression technique called non-negative matrix factorization (NMF). Several special properties are obtained as a result of the constrained optimization problem of NMF. For facial images, the additive nature of NMF results in a basis of features, such as eyes, noses, and lips. We explore various methods for efficiently computing NMF, placing particular emphasis on the initialization of current algorithms. We propose using Spherical K-Means clustering to produce a structured initialization for NMF. We demonstrate some of the properties that result from this initialization and develop an efficient way of choosing the rank of the low-dimensional NMF representation. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
non-negative matrix factorization
k-means clustering
constrained optimization
rank reduction
data mining
compression
feature extraction
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7.6
论文数:
1.3W
被引数:
4.5W
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