Project details for A Regularized Correntropy Framework for Robust Pattern Recognition

Logo A Regularized Correntropy Framework for Robust Pattern Recognition 1.0

by openpr_nlpr - June 3, 2013, 09:59:51 CET [ Project Homepage BibTeX Download ]

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Description:

This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classicalmean square error (MSE) criterion that is sensitive to outliers. Then an l1 regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1 regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Windows
Data Formats: None
Tags: L1 Minimization, Correntropy, Robust Sparse Representation
Archive: download here

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