OPTIMAL EXPERIMENTAL DESIGN IN CANONICAL EXPANSIONS WITH APPLICATIONS TO SIGNAL COMPRESSION Conference Paper

abstract

• 2016 IEEE. In this paper, we introduce an experimental design framework for Karhunen-Loeve compression. This method based on the concept of mean objective of uncertainty determines the best unknown parameter of the covariance matrix to be estimated first in order to improve the quality of the compressed signal. Moreover, we find the closed-form solution to the intrinsically Bayesian robust Karhunen-Love compression that is required for experimental design and provides the optimal signal compression on average relative to the uncertainty class of covariance matrices. We verify the performance of the proposed experimental design method for the case in which the covariance matrix consists of disjoint blocks.

name of conference

• 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

authors

• Dougherty, Edward
• Qian, Xiaoning

published proceedings

• 2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP)

author list (cited authors)

• Dehghannasiri, R., Qian, X., & Dougherty, E. R.

citation count

• 3

complete list of authors

• Dehghannasiri, Roozbeh||Qian, Xiaoning||Dougherty, Edward R

publication date

• January 2016

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)  Journal

keywords

• Canonical Expansion
• Intrinsically Bayesian Robust (ibr)
• Karhunen-loeve Compression
• Mean Objective Cost Of Uncertainty (mocu)
• Optimal Experimental Design

Digital Object Identifier (DOI)

• 10.1109/globalsip.2016.7905810

International Standard Book Number (ISBN) 13

• 9781509045457

start page

• 94

end page

• 98

URL

• http://dx.doi.org/10.1109/globalsip.2016.7905810