NDLI: Locality-based linear discriminant projection

Content Provider	IEEE Xplore Digital Library
Author	Ding Xinlong Huang Pu Chen Caikou
Copyright Year	2012
Description	Author affiliation: Information Engineering College, Yangzhou University, Yangzhou 225009, China (Ding Xinlong; Huang Pu; Chen Caikou)
Abstract	This paper presents a novel feature extraction technique, called locality-based linear discriminant projection (LLDP), for multi-class discriminant tasks. The proposed method can be regarded as an improved version of the classical linear discriminant projection (LDA), which is one of the most popular feature extraction methods. LLDP has at least two distinct advantages compared with LDA. Firstly, LDA may fail to discover the potential structure in the data, while LLDP integrates both class label information and neighborhood relationships between samples, so LLDP has the ability to reveal the intrinsic structure in the data which is more helpful to solve such nonlinear problem as recognition tasks than LDA. Secondly, LDA usually can only extract at most C-1 (C is the number of sample classes) features in a real situation, which doesn't suffice to categorized all samples, while LLDP is able to obtain much more features. Therefore, LLDP has more powerful discriminant ability than LDA. We carry out LLDP and LDA, as well as other current popular algorithms such as PCA, LPP and MFA on the ORL face database and the CENPARPARMI handwritten numerical database, and the experimental results show that LLDP can achieve much higher recognition rate.
Starting Page	3942
Ending Page	3946
File Size	195755
Page Count	5
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Handwriting recognition Databases linear discriminant projection (LDA) face recognition locality Feature extraction Vectors Nickel Face Principal component analysis
Content Type	Text
Resource Type	Article

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