Iterative subspace analysis based on feature line distance

Authors:
Yanwei Pang;Yuan Yuan;Xuelong Li
Affiliations:
School of Electronic Information Engineering, Tianjin University, Tianjin, China;School of Engineering and Applied Science, Aston University, Birmingham, U.K.;School of Computer Science and Information Systems, Birkbeck College, University of London, London, U.K.
Venue:
IEEE Transactions on Image Processing
Year:
2009

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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.