Total singular value decomposition. Robust SVD, regression and location-scale

TL;DR

This paper introduces Total SVD, a robust variation of traditional SVD that emphasizes component quality over approximation, incorporating weighting to handle gross errors and utilizing fixed point algorithms for computation.

Contribution

It proposes a new Total SVD method that improves robustness and component focus, addressing limitations of classical SVD in the presence of data errors.

Findings

Total SVD enhances robustness against gross data errors.

The method emphasizes component quality over approximation.

Algorithmic implementation relies on high-dimensional fixed point computations.

Abstract

Singular Value Decomposition (SVD) is the basic body of many statistical algorithms and few users question whether SVD is properly handling its job. SVD aims at evaluating the decomposition that best approximates a data matrix, given some rank restriction. However often we are interested in the best components of the decomposition rather than in the best approximation . This conflict of objectives leads us to introduce {\em Total SVD}, where the word "Total" is taken as in "Total" least squares. SVD is a least squares method and, therefore, is very sensitive to gross errors in the data matrix. We make SVD robust by imposing a weight to each of the matrix entries. Breakdown properties are excellent. Algorithmic aspects are handled; they rely on high dimension fixed point computations.