Feature Extraction Using Evolutionary Weighted Principal Component Analysis
Nan Liu, Han Wang · 2006
Principal component analysis (PCA) and Fisher's linear discriminant (FLD) are two commonly used feature extraction techniques. Based on them, an evolutionary weighted principal component analysis (EWPCA) is proposed. Similar to FLD, the proposed EWPCA maximizes the ratio of between-class scatter to that of within-class scatter, while keeps even smaller reconstruction error than that of traditional PCA. Genetic algorithms (GAs) are chosen as the searching method to select optimal weights for the EWPCA. In the face recognition application, Evolutionary Eigenface obtained by performing EWPCA, is used as the representation of original face images. Our experimental results prove that EWPCA outperforms both PCA and FLD. Besides, Evolutionary Cosineface is also proposed, which creates better classification performance than most reported approaches on ORLface database.