An improved study of locality sensitive discriminant analysis for object recognition

Liu Liu; Fuqiang Zhou; Yuzhu He

doi:10.1117/12.2186731

17 July 2015 An improved study of locality sensitive discriminant analysis for object recognition

Liu Liu, Fuqiang Zhou, Yuzhu He

Proceedings Volume 9524, International Conference on Optical and Photonic Engineering (icOPEN 2015); 95240O (2015) https://doi.org/10.1117/12.2186731
Event: International Conference on Optical and Photonic Engineering (icOPEN2015), 2015, Singapore, Singapore

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.

Citation Download Citation

Liu Liu, Fuqiang Zhou, and Yuzhu He "An improved study of locality sensitive discriminant analysis for object recognition", Proc. SPIE 9524, International Conference on Optical and Photonic Engineering (icOPEN 2015), 95240O (17 July 2015); https://doi.org/10.1117/12.2186731

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KEYWORDS

Databases

Object recognition

Principal component analysis

Data centers

Data modeling

Detection and tracking algorithms

Facial recognition systems

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