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Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis
Existing robust SVD algorithms often sacrifice speed for robustness or fail in the presence of only a few outliers.
Covariance-engaged Classification of Sets via Linear Programming
The convergence rates of estimation errors and risk of the CLIPS classifier are established to show that having multiple observations in a set leads to faster convergence rates, compared to the standard classification situation in which there is only one observation in the set.
Continuum directions for supervised dimension reduction
With a presence of binary supervision data, these directions continuously bridge the principal component, mean difference and linear discriminant directions, thus ranging from unsupervised to fully supervised dimension reduction.
A Statistical Approach to Set Classification by Feature Selection with Applications to Classification of Histopathology Images
In particular, the method of principal component analysis is used to extract the features of major variation.
