James Baglama | Searcharxiv

arXiv2024

A Hybrid Algorithm for Computing a Partial Singular Value Decomposition Satisfying a Given Threshold

In this paper, we describe a new hybrid algorithm for computing all singular triplets above a given threshold and provide its implementation in MATLAB/Octave and R. The high performance of our codes and ease at which they can be used, either independently or within a larger numerical scheme, are illustrated through several numerical examples with applications to matrix completion and image compression. Well-documented MATLAB and R codes are provided for public use.

math.NA↗

arXiv2016

Efficient Thresholded Correlation using Truncated Singular Value Decomposition

Efficiently computing a subset of a correlation matrix consisting of values above a specified threshold is important to many practical applications. Real-world problems in genomics, machine learning, finance other applications can produce correlation matrices too large to explicitly form and tractably compute. Often, only values corresponding to highly-correlated vectors are of interest, and those values typically make up a small fraction of the overall correlation matrix. We present a method based on the singular value decomposition (SVD) and its relationship to the data covariance structure that can efficiently compute thresholded subsets of very large correlation matrices.

stat.CO↗