Progressive Principal Component Analysis

Abstract. Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best reconstruction for an original data in the mean squared error sense. In this paper, the progressive PCA (PrPCA) is proposed, which could progressively extract features from a set of given data with large dimensionality and the extracted features are subsequently applied to pattern recognition. Experiments on the FERET database show its face recognition performance is better than those based on both E(PC)2 A and FLDA.

• Type: Conference
• Year: 2004
• Where: ISNN
• Authors: Jun Liu, Songcan Chen, Zhi-Hua Zhou
• Keywords: Feature Extraction Approach, ISNN 2004, Mean Squared Error, Principal Component Analysis