Motivated by the analysis of nonnegative data objects, a novel Nested Nonnegative Cone Analysis (NNCA) approach is proposed to overcome some drawbacks of existing methods. The application of traditional PCA/SVD method to nonnegative data often cause the approximation matrix leave the nonnegative...

Sparse Principal Component Analysis (PCA) methods are efficient tools to reduce the dimension (or number of variables) of complex data. Sparse principal components (PCs) are easier to interpret than conventional PCs, because most loadings are zero. We study the asymptotic properties of these...

A comparison of some binary discrimination methods is done in the high dimension low sample size context for Gaussian data with common diagonal covariance matrix. In particular we obtain results about the asymptotic behavior of the methods Support Vector Machine, Mean Difference (i.e. Centroid...