Local-information-based uncorrelated feature extraction

In the past few years, the computer vision and pattern recognition community has witnessed the rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Based on LPP, we propose a novel feature extraction method, called uncorrelated locality preserving projection (ULPP). We show that the extracted features via ULPP are statistically uncorrelated, which is desirable for many pattern analysis applications. We compare the proposed ULPP approach with LPP and principal component analysis (PCA) on the publicly available data sets, FERET and AR. Experimental results suggest that the proposed ULPP approach provides a better representation of the data and achieves much higher recognition accuracies.