Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density EstimationDownload PDF

21 May 2021, 20:50 (modified: 01 Jul 2022, 04:16)NeurIPS 2021 PosterReaders: Everyone
Keywords: Low-rank, nonparametric, density estimation, statistical learning theory, matrix factorization, tensor factorization, multi-view model
TL;DR: We propose and analyze a low rank approach to nonparametric density estimation and theoretically find that such an approach can offer huge improvements in sample complexity.
Abstract: The construction and theoretical analysis of the most popular universally consistent nonparametric density estimators hinge on one functional property: smoothness. In this paper we investigate the theoretical implications of incorporating a multi-view latent variable model, a type of low-rank model, into nonparametric density estimation. To do this we perform extensive analysis on histogram-style estimators that integrate a multi-view model. Our analysis culminates in showing that there exists a universally consistent histogram-style estimator that converges to any multi-view model with a finite number of Lipschitz continuous components at a rate of $\widetilde{O}(1/\sqrt[3]{n})$ in $L^1$ error. In contrast, the standard histogram estimator can converge at a rate slower than $1/\sqrt[d]{n}$ on the same class of densities. We also introduce a new nonparametric latent variable model based on the Tucker decomposition. A rudimentary implementation of our estimators experimentally demonstrates a considerable performance improvement over the standard histogram estimator. We also provide a thorough analysis of the sample complexity of our Tucker decomposition-based model and a variety of other results. Thus, our paper provides solid theoretical foundations for extending low-rank techniques to the nonparametric setting.
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