Nyström M-Hilbert-Schmidt independence criterion

Florian Kalinke and Zoltan Szabo

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Kernel techniques are among the most popular and powerful approaches of data science. Among the key features that make kernels ubiquitous are (i) the number of domains they have been designed for, (ii) the Hilbert structure of the function class associated to kernels facilitating their statistical analysis, and (iii) their ability to represent probability distributions without loss of information. These properties give rise to the immense success of Hilbert-Schmidt independence criterion (HSIC) which is able to capture joint independence of random variables under mild conditions, and permits closed-form estimators with quadratic computational complexity (w.r.t. the sample size). In order to alleviate the quadratic computational bottleneck in large-scale applications, multiple HSIC approximations have been proposed, however these estimators are restricted to M=2 random variables, do not extend naturally to the M≥2 case, and lack theoretical guarantees. In this work, we propose an alternative Nyström-based HSIC estimator which handles the M≥2 case, prove its consistency, and demonstrate its applicability in multiple contexts, including synthetic examples, dependency testing of media annotations, and causal discovery.

JEL-codes: C1 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2023-08-31
New Economics Papers: this item is included in nep-ecm
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Published in Proceedings of Machine Learning Research, 31, August, 2023, 216, pp. 1005-1015. ISSN: 2640-3498

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