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 ...

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 ...

We present a new computational approach to approximating a large, noisy data table by a low-rank matrix with sparse singular vectors. The approximation is obtained from thresholded subspace iterations ...

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 ...