Parallel algorithms for singular value decomposition (SVD) have risen to prominence as an indispensable tool in high-performance numerical linear algebra. They offer significant improvements in the ...
Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...
SIAM Journal on Numerical Analysis, Vol. 13, No. 1 (Mar., 1976), pp. 76-83 (8 pages) Two generalization of the singular value decomposition are given. These generalizations provided a unified way of ...
Robust location and covariance estimators are developed via general M estimation for covariance matrix eigenvectors and eigenvalues. The solution to this GM estimation problem is obtained by ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback