DocumentCode :
1190531
Title :
Iterative Subspace Analysis Based on Feature Line Distance
Author :
Pang, Yanwei ; Yuan, Yuan ; Li, Xuelong
Author_Institution :
Sch. of Electron. Inf. Eng., Tianjin Univ., Tianjin
Volume :
18
Issue :
4
fYear :
2009
fDate :
4/1/2009 12:00:00 AM
Firstpage :
903
Lastpage :
907
Abstract :
Nearest feature line-based subspace analysis is first proposed in this paper. Compared with conventional methods, the newly proposed one brings better generalization performance and incremental analysis. The projection point and feature line distance are expressed as a function of a subspace, which is obtained by minimizing the mean square feature line distance. Moreover, by adopting stochastic approximation rule to minimize the objective function in a gradient manner, the new method can be performed in an incremental mode, which makes it working well upon future data. Experimental results on the FERET face database and the UCI satellite image database demonstrate the effectiveness.
Keywords :
image recognition; iterative methods; mean square error methods; stochastic processes; generalization performance; incremental analysis; iterative subspace analysis; mean square feature line distance; nearest feature line; objective function; projection point; stochastic approximation rule; Face recognition; Image analysis; Image databases; Interpolation; Linear discriminant analysis; Principal component analysis; Scattering; Stochastic processes; Tensile stress; Training data; Face recognition; feature line; subspace;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2008.2011167
Filename :
4799382
Link To Document :
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