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Int. J. Fuzzy Log. Intell. Syst. 2018; 18(2): 120-125

Published online June 25, 2018

https://doi.org/10.5391/IJFIS.2018.18.2.120

© The Korean Institute of Intelligent Systems

Combining Locality Preserving Projection with Global Information for Efficient Recognition

Heesung Lee

Department of Railroad Electrical and Electronics Engineering, Korea National University of Transportation, Uiwang-si, Korea

Correspondence to :
Heesung Lee (hslee0717@ut.ac.kr)

Received: May 8, 2018; Revised: June 1, 2018; Accepted: June 7, 2018

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

This paper proposes a new feature extraction scheme, combining global and local features. The proposed method uses principal component analysis (PCA) and linear discriminant analysis (LDA) for global property and locality preserving projections (LPP) for local property of the pattern. PCA and LDA are known for extracting the most descriptive ones after projection while LPP is known for preserving the neighborhood structure of the data set. The proposed combing method integrates global and local descriptive information and finds an efficient set of alternatives beyond PCA, LDA and LPP in the parametric space. Further, In order to find optimal parameters, the genetic algorithm (GA) is employed. Experiments are performed with four data sets selected in UCI machine learning repository to show the performance of the proposed algorithm.

Keywords: PCA, LDA, LPP, GA, Feature space, UCI machine learning repository

No potential conflict of Interest relevant to this article was reported.

Heesung Lee received the B.S., M.S., and Ph.D. degrees in Electrical and Electronic Engineering from Yonsei University, Seoul, Korea, in 2003, 2005, and 2010, respectively. From 2011 to 2014, he was a managing researcher with the S1 Corporation, Seoul, Korea. Since 2015, he has been with the railroad electrical and electronics engineering at Korea National University of Transportation, Uiwang-si, Gyeonggi-do, Korea, where he is currently an assistant professor. His current research interests include computational intelligence, biometrics, and intelligent railroad system.

E-mail: hslee0717@ut.ac.kr


Article

Original Article

Int. J. Fuzzy Log. Intell. Syst. 2018; 18(2): 120-125

Published online June 25, 2018 https://doi.org/10.5391/IJFIS.2018.18.2.120

Copyright © The Korean Institute of Intelligent Systems.

Combining Locality Preserving Projection with Global Information for Efficient Recognition

Heesung Lee

Department of Railroad Electrical and Electronics Engineering, Korea National University of Transportation, Uiwang-si, Korea

Correspondence to:Heesung Lee (hslee0717@ut.ac.kr)

Received: May 8, 2018; Revised: June 1, 2018; Accepted: June 7, 2018

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes a new feature extraction scheme, combining global and local features. The proposed method uses principal component analysis (PCA) and linear discriminant analysis (LDA) for global property and locality preserving projections (LPP) for local property of the pattern. PCA and LDA are known for extracting the most descriptive ones after projection while LPP is known for preserving the neighborhood structure of the data set. The proposed combing method integrates global and local descriptive information and finds an efficient set of alternatives beyond PCA, LDA and LPP in the parametric space. Further, In order to find optimal parameters, the genetic algorithm (GA) is employed. Experiments are performed with four data sets selected in UCI machine learning repository to show the performance of the proposed algorithm.

Keywords: PCA, LDA, LPP, GA, Feature space, UCI machine learning repository

Fig 1.

Figure 1.

Structure of the chromosome.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 120-125https://doi.org/10.5391/IJFIS.2018.18.2.120

Fig 2.

Figure 2.

Correct classification ratio for four databases of the UCI repository.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 120-125https://doi.org/10.5391/IJFIS.2018.18.2.120
procedure Genetic Algorithm
 begin
  initialize P(t);
  while termination-condition not satisfied do
  begin
   evaluate P(t);
   select P(t + 1) from P(t);
   crossover P(t + 1);
   mutation P(t + 1);
   t = t + 1;
  end
 end

Table 1. Databases used for experimnets.

DatabaseNumber of instancesNumber of classesNumber of features
Sonar208360
Spambase4601257
SPECTF267244
Heart270213

Table 2. GA parameters.

ParameterValue
Crossover rate0.6
Mutation rate0.05
Population size100
Generation200

Table 3. Average, the best, and the worst accrucy using PCA.

DatabaseAverageBestWorst
Sonar82.35 (0.053)86.2774.51
Spambase81.84 (0.004)82.3581.39
SPECTF69.70 (0.081)74.2457.58
Heart62.31 (0.022)64.1759.70

Table 4. Average, the best, and the worst accrucy using LDA.

DatabaseAverageBestWorst
Sonar74.02 (0.079)80.4062.75
Spambase78.37 (0.022)81.6577.13
SPECTF73.11 (0.054)75.7665.15
Heart53.36 (0.056)59.7046.27

Table 5. Average, the best, and the worst accrucy using LPP.

DatabaseAverageBestWorst
Sonar84.80 (0.040)90.2080.39
Spambase82.04 (0.002)82.2681.82
SPECTF73.86 (0.050)80.3068.19
Heart55.97 (0.019)58.2153.73

Table 6. Average, the best, and the worst accrucy of the proposed method.

DatabaseAverageBestWorst
Sonar86.27 (0.679)92.1676.47
Spambase83.13 (0.009)83.5782.26
SPECTF75.37 (0.084)83.3363.64
Heart63.43 (0.026)65.6759.70

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