Rank estimation for (approximately) low-rank matrices
Pages 30 - 32
Abstract
In observational data analysis, e.g., causal inference, one often encounters data sets that are noisy and incomplete, but come from inherently "low rank" (or correlated) systems. Examples include user ratings of movies/products and term frequency matrices for documents amongst others. In such analysis, estimating the approximate rank of the data sets serves an important function of delineating the signal from the noise. In this paper, we propose a technique to estimate the rank of observational data matrices, compare it to previously proposed techniques, and make a specific methodological contribution of improving the algorithmic parameter estimation in the robust synthetic control method in [1].
References
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Shashanka Ubaru and Yousef Saad. Fast methods for estimating the numerical rank of large matrices. In International Conference on Machine Learning, pages 468--477. PMLR, 2016.
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Anish Agarwal, Abdullah Alomar, Romain Cosson, Devavrat Shah, and Dennis Shen. Synthetic Interventions. https://arxiv.org/pdf/2006.07691.pdf.
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M. Amjad, V. Misra, D. Shah, and D. Shen. mRSC: Multi-dimensional robust synthetic control. In Proceedings of the ACM on Measurement and Analysis of Computing Systems (Sigmetrics 2019), volume 3, page 37, June 2019.
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Association for Computing Machinery
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Publication History
Published: 20 January 2022
Published in SIGMETRICS Volume 49, Issue 2
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