Title: Improving Non-negative Matrix Factorizations Through Structured Initialization
Authors: Stefan Wild, James Curry, Anne Dougherty
Abstract: 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 e4ciently 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 e4cient way of choosing the rank of the low-dimensional NMF representation.
Keywords: Non-negative matrix factorization, K-means clustering, Constrained optimization, Rank reduction, Data mining, Compression, Feature extraction
Thanks: This work initiated under NSF VIGRE Grant No. DMS-9810751
Status: Appears in Pattern Recognition, Volume 37, Issue 11, November 2004, Pages 2217-2232.
Link: [PDF via Science Direct]
BibTeX:
@article{SWJCAD04,
    author      = "Stefan M. Wild and James H. Curry and Anne Dougherty",
    title       = "Improving Non-negative Matrix Factorizations Through Structured Initialization",
    journal     = "Pattern Recognition",
    volume      = "37",
    number      = "11",
    month       = "November",    
    year        = "2004",
    pages       = "2217--2232",
    doi         = "http://dx.doi.org/10.1016/j.patcog.2004.02.013"
}
	
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