mloss.org Principal Component Analysis Based on Nonparametric Maximum Entropy http://mloss.orgUpdates and additions to Principal Component Analysis Based on Nonparametric Maximum Entropy enFri, 02 Dec 2011 05:45:02 -0000Principal Component Analysis Based on Nonparametric Maximum Entropy 1.0.0http://mloss.org/software/view/361/<html><p>In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEnt-PCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction error either based on L2-norm or L1-norm, the MaxEnt-PCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEnt-PCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEnt-PCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods. </p></html>Ran HeFri, 02 Dec 2011 05:45:02 -0000http://mloss.org/software/rss/comments/361http://mloss.org/software/view/361/pattern recognition