NDLI: Evaluation of Distance Measures for NMF-based Face Recognition

Content Provider	IEEE Xplore Digital Library
Author	Yun Xue Chong Sze Tong Weipeng Zhang
Copyright Year	2006
Description	Author affiliation: Dept. of Math., Hong Kong Baptist Univ. (Yun Xue; Chong Sze Tong)
Abstract	Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that can extract parts from visual data. The goal of this technique is to find intuitive basis such that training examples can be faithfully reconstructed using linear combination of basis images which are restricted to non-negative values. Thus NMF basis images can be understood as localized features that correspond better with intuitive notions of parts of images. However, there has not been any systematic study to identify suitable distance measure for data classification in this space defined by the NMF basis images. In this article we evaluate the performance of 17 distance measures (which include most of the standard distance measures used in face recognition, as well as a new non-negative vector similarity coefficient-based (NVSC) distance that we advocate for use in NMF-based pattern recognition) between feature vectors based on the result of the non-negative matrix factorization (NMF) algorithm for face recognition. Recognition experiments are performed using the MIT-CBCL database, CMU AMP Face Expression database and YaleB database. The experiments show, that our NVSC distance is consistently among the best measures with respect to different databases. Moreover, using this new distance, we almost always achieve better result than the L1, L2 and Mahalanobis distance which are often used in pattern recognition
Starting Page	651
Ending Page	656
File Size	205929
Page Count	6
File Format	PDF
ISBN	1424406048
DOI	10.1109/ICCIAS.2006.294216
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonnegative Matrix Factorization Image databases Face recognition Measurement standards Spatial databases Mathematics Pattern recognition Data mining Distance measures Unsupervised learning Image reconstruction Principal component analysis
Content Type	Text
Resource Type	Article

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