Authors:Ran He

A Regularized Correntropy Framework for Robust Pattern ...

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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 sensiti ...

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AuthorsRan He
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Efficient Nonnegative Sparse Coding Algorithm

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Nonnegative Sparse Coding, Discriminative Semi-supervised Learning, sparse probability graph

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Half-quadratic based Iterative Minimization for Robust ...

Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, ...

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AuthorsRan He
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L21 Regularized Correntropy for Robust Feature Selectio...

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We study the problem of robust feature extraction based on L21 regularized correntropy in both theoretical and algorithmic manner. In theoretical part, we point out that an L21-norm minimization can be justified from the viewpoint of half-quadratic (HQ) o ...

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AuthorsRan He
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Maximum Correntropy Criterion for Robust Face Recogniti...

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This code is developed based on Uriel Roque's active set algorithm for the linear least squares problem with nonnegative variables in: Portugal, L.; Judice, J.; and Vicente, L. 1994. A comparison of block pivoting and interior-point algorithms for linear ...

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AuthorsRan HE
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Principal Component Analysis Based on Nonparametric Max...

In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEnt-PCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction ...

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AuthorsRan He
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Sparse representation (L1 minimization) via half-quadra...

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Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, ...

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AuthorsRan He
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Two-stage Sparse Representation

This program implements a novel robust sparse representation method, called the two-stage sparse representation (TSR), for robust recognition on a large-scale database. Based on the divide and conquer strategy, TSR divides the procedure of robust recogni ...

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AuthorsRan HE
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