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Proceedings Paper

An improved study of locality sensitive discriminant analysis for object recognition
Author(s): Liu Liu; Fuqiang Zhou; Yuzhu He
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Paper Abstract

Locality sensitive discriminant analysis (LSDA) is a method considering both the discriminant and geometrical structure of the data. Within-class graph and between-class graph are first constructed to discover both geometrical and discriminant structure of the data manifold. Then a proportional constant is used to measure the different importance of two graphs. Finally, a reasonable criterion is used to choose a good map so that the connected points of within-class graph stay as close as possible while connected points of between-class graph stay as distant as possible. The key technique of LSDA is nearest neighbor graph construction. In this paper, we compared two different nearest neighbor graph construction methods. The experiment results demonstrate that splitting a nearest neighbor into equally sized with class graph and between-class graph has smaller amount of computations while construct within-class graph and between-class graph by using different sized nearest neighbors could improving the accuracy.

Paper Details

Date Published: 17 July 2015
PDF: 8 pages
Proc. SPIE 9524, International Conference on Optical and Photonic Engineering (icOPEN 2015), 95240O (17 July 2015); doi: 10.1117/12.2186731
Show Author Affiliations
Liu Liu, BeiHang Univ. (China)
Fuqiang Zhou, BeiHang Univ. (China)
Yuzhu He, BeiHang Univ. (China)

Published in SPIE Proceedings Vol. 9524:
International Conference on Optical and Photonic Engineering (icOPEN 2015)
Anand K. Asundi; Yu Fu, Editor(s)

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