| 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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