Least-Square Regularized Regression in Compressed Domain

Weijun Lu; Yi Tang; Hong Chen

doi:10.1109/iptc.2010.27

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JOURNAL ARTICLE

Least-Square Regularized Regression in Compressed Domain

Weijun Lu Yi Tang Hong Chen

Year: 2010 Vol: 3 Pages: 93-96

DOI: 10.1109/iptc.2010.27

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Abstract

This paper considers the regularized learning algorithm associated with the least-square loss and compressed domain. The target is the error analysis for the regression problem learned in compressed domain. We show that the least-square regularized algorithm is beneficial from the compressed sensing.

Keywords:

Compressed sensing Square (algebra) Mean squared error Domain (mathematical analysis) Computer science Regression Algorithm Frequency domain Mathematics Artificial intelligence Statistics Computer vision Mathematical analysis

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Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

Photoacoustic and Ultrasonic Imaging

Physical Sciences → Engineering → Biomedical Engineering

Electrical and Bioimpedance Tomography

Physical Sciences → Engineering → Electrical and Electronic Engineering

Least-Square Regularized Regression in Compressed Domain

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