Compressive parameter estimation with earth mover's distance via K-median clustering

Dian Mo; Marco F. Duarte

doi:10.1117/12.2024247

26 September 2013 Compressive parameter estimation with earth mover's distance via K-median clustering

Dian Mo, Marco F. Duarte

Proceedings Volume 8858, Wavelets and Sparsity XV; 88581P (2013) https://doi.org/10.1117/12.2024247
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

In recent years, sparsity and compressive sensing have attracted significant attention in parameter estimation tasks, including frequency estimation, delay estimation, and localization. Parametric dictionaries collect observations for a sampling of the parameter space and can yield sparse representations for the signals of interest when the sampling is sufficiently dense. While this dense sampling can lead to high coherence in the dictionary, it is possible to leverage structured sparsity models to prevent highly coherent dictionary elements from appearing simultaneously in a signal representation, alleviating these coherence issues. However, the resulting approaches depend heavily on a careful setting of the maximum allowable coherence; furthermore, their guarantees apply to the coefficient vector recovery and do not translate in general to the parameter estimation task. We propose a new algorithm based on optimal sparse approximation measured by earth mover's distance (EMD). We show that EMD provides a better-suited metric for the performance of parametric dictionary-based parameter estimation. We leverage K-median clustering algorithms to solve the EMD-optimal sparse approximation problem, and show that the resulting compressive parameter estimation algorithms provide satisfactory performance without requiring control of dictionary coherence.

Citation Download Citation

Dian Mo and Marco F. Duarte "Compressive parameter estimation with earth mover's distance via K-median clustering", Proc. SPIE 8858, Wavelets and Sparsity XV, 88581P (26 September 2013); https://doi.org/10.1117/12.2024247

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available