|Table of Contents|

Probabilistic classification preserving projections and its application to face recognition

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2013年01期
Page:
7-
Research Field:
Publishing date:

Info

Title:
Probabilistic classification preserving projections and its application to face recognition
Author(s):
Yang ZhangjingLiu ChuancaiGu XingjianZhu Jun
School of Computer Science and Engineering,NUST,Nanjing 210094,China
Keywords:
face recognitionfeature extractiondimensionality reductionmanifoldlocality preserving projections
PACS:
TP391.4
DOI:
-
Abstract:
In order to improve the ability of low dimensional space to represent highdimensional samples,a novel manifold learning method called probabilistic classification maintain projection(PCPP) is proposed.The PCPP takes class information into account and refines similarity weights of intraclass samples,which not only contain neighborhood information of samples,but also can reflect the probability that a sample can be correctly classified when its K nearest neighbors are selected.After projection,neighborhood relationship of the intraclass samples which possess more classification probability can be preserved.Experimental results on the Yale,FERET and AR face databases demonstrate that the PCPP performs better than other algorithms.

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Last Update: 2013-02-15