The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the au...
—This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formu...
In data-analysis problems with a large number of dimension, principal component analysis based on L2-norm (L2PCA) is one of the most popular methods, but L2-PCA is sensitive to out...
Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivit...
This paper proposes a new facial expression recognition method which combines Higher Order Local Autocorrelation (HLAC) features with Weighted PCA. HLAC features are computed at ea...