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  1. No Access

    Book

    Robust Recognition via Information Theoretic Learning

    Ran He, Baogang Hu, **aotong Yuan… in SpringerBriefs in Computer Science (2014)

  2. No Access

    Chapter

    Introduction

    Robust data classification or representation is a fundamental task and has a long history in computer vision. The algorithmic robustness, which is derived from the statistical definition of a breakdown point [...

    Ran He, Baogang Hu, **aotong Yuan… in Robust Recognition via Information Theoret… (2014)

  3. No Access

    Chapter

    M-Estimators and Half-Quadratic Minimization

    In robust statistics, there are several types of robust estimators, including M-estimator (maximum likelihood type estimator), L-estimator (linear combinations of order statistics), R-estimator (estimator base...

    Ran He, Baogang Hu, **aotong Yuan… in Robust Recognition via Information Theoret… (2014)

  4. No Access

    Chapter

    Correntropy and Linear Representation

    The nearest neighbor (NN) classifier is the most popular method for image-based object recognition. In NN classifier, the representational capacity of an image database and the recognition rate depend on how r...

    Ran He, Baogang Hu, **aotong Yuan… in Robust Recognition via Information Theoret… (2014)

  5. No Access

    Chapter

    Correntropy with Nonnegative Constraint

    Nonnegativity constraint is more consistent with the biological modeling of visual data and often leads to better performance for data representation and graph learning [66]. In this chapter, we present an ove...

    Ran He, Baogang Hu, **aotong Yuan… in Robust Recognition via Information Theoret… (2014)

  6. No Access

    Chapter

    Information Measures

    Information theoretic learning (ITL) was initiated in the late 1990s at CNEL [126]. It uses descriptors from information theory (entropy and divergences) estimated directly from the data to substitute the conv...

    Ran He, Baogang Hu, **aotong Yuan… in Robust Recognition via Information Theoret… (2014)

  7. No Access

    Chapter

    ℓ 1 Regularized Correntropy

    Sparse signal representation arises in application of compressed sensing and has been considered as a significant technique in computer vision and machine learning [27, 65, 154]. Based on the ℓ 0-ℓ ...

    Ran He, Baogang Hu, **aotong Yuan… in Robust Recognition via Information Theoret… (2014)

  8. No Access

    Chapter and Conference Paper

    Reconstruction of Tree Crown Shape from Scanned Data

    Reconstruction of a real tree from scattered scanned points is a new challenge in virtual reality. Although many progresses are made on main branch structures and overall shape of a tree, reconstructions are s...

    Chao Zhu, **aopeng Zhang, Baogang Hu… in Technologies for E-Learning and Digital En… (2008)

  9. No Access

    Chapter and Conference Paper

    Sparse Kernel Fisher Discriminant Analysis

    This paper presents a method of Sparse Kernel Fisher Discriminant Analysis (SKFDA) through approximating the implicit within-class scatter matrix in feature space. Traditional Kernel Fisher Discriminant Analysis ...

    Hongjie **ng, Yujiu Yang, Yong Wang, Baogang Hu in Advances in Neural Networks – ISNN 2005 (2005)

  10. No Access

    Article

    Fast construction of plant architectural models based on substructure decomposition

    Plant structure, representing the physical link among different organs, includes many similar substructures. In this paper, a new method is presented to construct plant architectural models of most plant speci...

    Hong** Yan, Philippe de Reffye… in Journal of Computer Science and Technology (2003)