Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary-class independent LDA as a pre-process for improving classification
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Pre-processing of classification data can be helpful regardless of the type of classifier. The objective of this pre-processing step is to achieve a high degree of separation among classes before the classifier is trained or tested. This results into a trace ratio problem which is difficult to solve. Methods such as Linear Discriminant Analysis (LDA) have already been used for the solution of this problem by turning it into a simpler yet inexact problem. Also, in classical LDA, the covariances of different classes are assumed to be similar, which is not the case in real-world problems. In this paper, a class-dependent approach to finding the linear transformation is proposed. This method solves the trace ratio problem directly and also removes the requirement of similar covariance matrices. While giving good results, the method is computationally expensive. To reduce the computational cost while maintaining the benefits of the class-dependent method, a multi-objective formulation is proposed and solved using NSGA-II. Simulation results show great improvement in classification using various classifiers.