Weighted principal component extraction with genetic algorithms

Authors:
Nan Liu;Han Wang
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
Venue:
Applied Soft Computing
Year:
2012

Citing 40
Cited 0

Principal component neural networks: theory and applications

Principal component neural networks: theory and applications
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Pursuit and Its Application to Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA versus LDA

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using the Discrete Cosine Transform

International Journal of Computer Vision - Special issue: Research at McGill University
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Feature Subset Selection Using a Genetic Algorithm

IEEE Intelligent Systems
A Framework for Robust Subspace Learning

International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds

The Journal of Machine Learning Research
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Toward Integrating Feature Selection Algorithms for Classification and Clustering

IEEE Transactions on Knowledge and Data Engineering
Recognize High Resolution Faces: From Macrocosm to Microcosm

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)

Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Robust locally linear embedding

Pattern Recognition
Classification by evolutionary ensembles

Pattern Recognition
Solving the small sample size problem in face recognition using generalized discriminant analysis

Pattern Recognition
Weighted and robust learning of subspace representations

Pattern Recognition
A study on three linear discriminant analysis based methods in small sample size problem

Pattern Recognition
Principal component analysis in ECG signal processing

EURASIP Journal on Applied Signal Processing
SubXPCA and a generalized feature partitioning approach to principal component analysis

Pattern Recognition
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Feature extraction using constrained maximum variance mapping

Pattern Recognition
Locally linear discriminant embedding: An efficient method for face recognition

Pattern Recognition
Feature Selection with Kernel Class Separability

IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear dimensionality reduction using relevance weighted LDA

Pattern Recognition
Face recognition based on multi-class mapping of Fisher scores

Pattern Recognition
Rapid and brief communication: Evolutionary extreme learning machine

Pattern Recognition
Handbook of Face Recognition

Handbook of Face Recognition
Dimensionality reduction using genetic algorithms

IEEE Transactions on Evolutionary Computation
Evolutionary discriminant analysis

IEEE Transactions on Evolutionary Computation
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
High-speed face recognition based on discrete cosine transform and RBF neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.