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arXiv · 1512.07246

Efficient Thresholded Correlation using Truncated Singular Value Decomposition

James Baglama·Michael Kane·Bryan Lewis·Alex Poliakov
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Abstract

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.

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James Baglama, Michael Kane, Bryan Lewis, Alex Poliakov. 2016-03-12. Efficient Thresholded Correlation using Truncated Singular Value Decomposition. https://arxiv.org/abs/1512.07246

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