Hybrid Manifold Embedding

    Authors

    Y. Liu; Y. Liu; K. C. C. Chan;K. A. Hua

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Neural Netw. Learn. Syst.

    Keywords

    Dimensionality reduction; geodesic clustering (GC); hybrid manifold; embedding (HyME); locally conjugate discriminant projection (LCDP); supervised manifold learning; NONLINEAR DIMENSIONALITY REDUCTION; LINEAR DISCRIMINANT-ANALYSIS; FACE; RECOGNITION; REPRESENTATION; PROJECTION; FRAMEWORK; DATABASE; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

    Abstract

    In this brief, we present a novel supervised manifold learning framework dubbed hybrid manifold embedding (HyME). Unlike most of the existing supervised manifold learning algorithms that give linear explicit mapping functions, the HyME aims to provide a more general nonlinear explicit mapping function by performing a two-layer learning procedure. In the first layer, a new clustering strategy called geodesic clustering is proposed to divide the original data set into several subsets with minimum nonlinearity. In the second layer, a supervised dimensionality reduction scheme called locally conjugate discriminant projection is performed on each subset for maximizing the discriminant information and minimizing the dimension redundancy simultaneously in the reduced low-dimensional space. By integrating these two layers in a unified mapping function, a supervised manifold embedding framework is established to describe both global and local manifold structure as well as to preserve the discriminative ability in the learned subspace. Experiments on various data sets validate the effectiveness of the proposed method.

    Journal Title

    Ieee Transactions on Neural Networks and Learning Systems

    Volume

    25

    Issue/Number

    12

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    2295

    Last Page

    2302

    WOS Identifier

    WOS:000345518900015

    ISSN

    2162-237X

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