Locality Preserving Fisher Discriminant Analysis with Clustering

DOI

10.2991/iccasm.2012.66How to use a DOI?

Keywords

Fisher discriminant analysis, Locality preserving, Feature extraction, Clustering

Abstract

Fisher discriminant analysis (FDA) is an important feature extraction method for many classifiers. However, it tends to give undesired results if samples in some classes form several separate clusters, i.e., multimodal. This paper proposed a new feature extraction method called locality preserving Fisher discriminant analysis with clustering (LPFDA) for multimodal data. First new classes are formed by clustering data according to labels, then the between-subclass scatter matrix and within-subclass scatter matrix are computed by new classes, finally the vectors are choose which will maximize the Fisher criterion function as the discriminant vector . When our method is applied to the recognition problems of digits and images, and the experimental results show the better performance than the original one.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lishan Zou
AU  - Yuechao Wang
AU  - Zhenzhou Chen
AU  - Xiaorong Wu
PY  - 2012/08
DA  - 2012/08
TI  - Locality Preserving Fisher Discriminant Analysis with Clustering
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 265
EP  - 268
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.66
DO  - 10.2991/iccasm.2012.66
ID  - Zou2012/08
ER  -