NDLI: Laplacian Discriminant Projection with Optimized Kernels for Supervised Feature Extraction and Classification

Content Provider	IEEE Xplore Digital Library
Author	Jun-Bao Li Shu-Chuan Chu Jeng-Shyang Pan
Copyright Year	2007
Description	Author affiliation: Harbin Inst. of Technol., Harbin (Jun-Bao Li)
Abstract	A novel feature extraction method, namely Laplacian discriminant projection with optimized kernels (KLDP-Opt) algorithm is proposed in this paper. The advantage of KLDP-Opt lies in: 1) the similarity matrix is constructed with the class-wise nonparametric similarity measure where it solves procedure selection problem; 2) data-dependent kernel is applied to solve the limitation of linearity of LPP, where the adaptive parameters of the data-dependent kernel are computed through optimizing an objective function designed for measuring the class separability of data in the feature space. Besides the theory derivation, the experiments are implemented on ORL and Yale face databases to evaluate the feasibility of the proposed algorithm.
Starting Page	115
Ending Page	120
File Size	156953
Page Count	6
File Format	PDF
ISBN	9780769529769
DOI	10.1109/ISDA.2007.102
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-20
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Algorithm design and analysis Laplace equations Machine learning algorithms Face recognition Machine learning Feature extraction Kernel Design optimization Information analysis
Content Type	Text
Resource Type	Article

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