PCA-based Feature Extraction using Class Information
Myoung Soo Park, Jin Hee Na, Jin‐Young Choi · 2006
Feature extraction is necessary to classify a data with large dimension such as image data. It is important that the obtained features include the maximum information of input data. The representative methods for feature extraction are PCA, ICA, LDA and MLP etc. PCA, LDA are unsupervised type algorithms, and LDA, MLP are supervised type algorithms. Supervised type algorithms are more suitable for feature extraction because of using input data with class information. In this paper, we suggest the feature extraction scheme which uses class information to extract features by PCA. We test our algorithm using Yale face database and analyze the performance to compare with other algorithms.