Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Study of Linear and Nonlinear Feature Extraction Methods
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Journal of Cognitive Neuroscience
Feature extraction using class-augmented principal component analysis (CA-PCA)
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
High-speed face recognition based on discrete cosine transform and RBF neural networks
IEEE Transactions on Neural Networks
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Algorithm learning based neural network integrating feature selection and classification
Expert Systems with Applications: An International Journal
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In this paper, we present a theoretical analysis on a novel supervised feature extraction method called class-augmented principal component analysis (CA-PCA), which is composed of processes for encoding the class information, augmenting the encoded information to data, and extracting features from class-augmented data by applying PCA. Through a combination of these processes, CA-PCA can extract features appropriate for classification. Our theoretical analysis aims to clarify the role of these processes and to provide an explanation on how CA-PCA can extract good features. Experimental results for various datasets are provided in order to show the validity of the proposed method for real problems. The effect of parameters on the quality of extracted features is also investigated and the rules of thumb for determining the appropriate parameters are provided.