Paper Title:
PSO-Based Uncorrelated Hybrid Discriminant Analysis Algorithm
  Abstract

Hybrid discriminant analysis (HDA) can overcome small sample problems and outperform PCA and LDA by unifying principal component analysis (PCA) and linear discriminant analysis (LDA) in a single framework. However, the existing HDA algorithm can’t extract more discriminant information from dataset, and model parameters are difficult to select. To deal with the above problems, a particle swarm optimal (PSO)-based uncorrelated hybrid discriminant analysis algorithm is presented. The conjugate orthogonal condition is added to optimization problem of HDA, PSO is explored to select optimal HDA parameters and the optimal solution can be achieved by solving eigenvalue problem. Simulation demonstrates merits of the proposed algorithm.

  Info
Periodical
Chapter
Chapter 6: Materials and Mechanics Information System
Edited by
Yongping Zhang, Linhua Zhou and Elwin Mao
Pages
671-675
DOI
10.4028/www.scientific.net/AMM.109.671
Citation
X. P. Liu, G. Y. Xu, "PSO-Based Uncorrelated Hybrid Discriminant Analysis Algorithm", Applied Mechanics and Materials, Vol. 109, pp. 671-675, 2012
Online since
October 2011
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Price
$32.00
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