BIOMEDICAL IMAGING GROUP (BIG)
Laboratoire d'imagerie biomédicale (LIB)

Bona Fide Riesz Projections for Density Estimation

P. del Aguila Pla, M. Unser

Proceedings of the Forty-Seventh IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'22), Singapore, Republic of Singapore, May 22-27, 2022, pp. 5613–5616.

The projection of sample measurements onto a reconstruction space represented by a basis on a regular grid is a powerful and simple approach to estimate a probability density function. In this paper, we focus on Riesz bases and propose a projection operator that, in contrast to previous works, guarantees the bona fide properties for the estimate, namely, non-negativity and total probability mass 1. Our bona fide projection is defined as a convex problem. We propose solution techniques and evaluate them. Results suggest an improved performance, specifically in circumstances prone to rippling effects. 

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AUTHOR="del Aguila Pla, P. and Unser, M.",
TITLE="{\textit{Bona Fide}} {R}iesz Projections for Density Estimation",
BOOKTITLE="Proceedings of the Forty-Seventh IEEE International
	Conference on Acoustics, Speech, and Signal Processing
	({ICASSP'22})",
YEAR="2022",
editor="",
volume="",
series="",
pages="5613--5616",
address="Singapore, Republic of Singapore",
month="May 22-27,",
organization="",
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