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Poster of the paper "Multivariate Density Estimation Using Low-Rank Fejér-Riesz Factorization"

DOI:
10.60864/g87h-va96
Citation Author(s):
Paris Karakasis, Nicholas Sidiropoulos
Submitted by:
Paris Karakasis
Last updated:
6 June 2024 - 10:54am
Document Type:
Poster
Document Year:
2024
Event:
Presenters:
Paris A. Karakasis
Paper Code:
SPTM-P5.2
 

We consider the problem of learning smooth multivariate probability density functions. We invoke the canonical decomposition of multivariate functions and we show that if a joint probability density function admits a truncated Fourier series representation, then the classical univariate Fejér-Riesz Representation Theorem can be used for learning bona fide joint probability density functions. We propose a scalable, flexible, and direct framework for learning smooth multivariate probability density functions even from potentially incomplete datasets. We demonstrate the effectiveness of the proposed framework by comparing it to several popular state-of-the-art methods.

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