Adaptive nonlinear manifolds and their applications to pattern recognition

Hujun Yin, Weilin Huang

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    Dimensionality reduction has long been associated with retinotopic mapping for understanding cortical maps. Multisensory information is processed, fused and mapped to an essentially 2-D cortex in an information preserving manner. Data processing and projection techniques inspired by this biological mechanism are playing an increasingly important role in pattern recognition, computational intelligence, data mining, information retrieval and image recognition. Dimensionality reduction involves reduction of features or volume of data and has become an essential step of information processing in many fields. The topic of manifold learning has recently attracted a great deal of attention, and a number of advanced techniques for extracting nonlinear manifolds and reducing data dimensions have been proposed from statistics, geometry theory and adaptive neural networks. This paper provides an overview of this challenging and emerging topic and discusses various recent methods such as self-organizing map (SOM), kernel PCA, principal manifold, isomap, local linear embedding, and Laplacian eigenmap. Many of them can be considered in a learning manifold framework. The paper further elaborates on the biologically inspired SOM model and its metric preserving variant ViSOM under the framework of adaptive manifold; and their applications in dimensionality reduction with face recognition are investigated. The experiments demonstrate that adaptive ViSOM-based methods produce markedly improved performance over the others due to their metric scaling and preserving properties along the nonlinear manifold. © 2010 Published by Elsevier Inc.
    Original languageEnglish
    Pages (from-to)2649-2662
    Number of pages13
    JournalInformation Sciences
    Issue number14
    Publication statusPublished - 15 Jul 2010


    • Biologically inspired models
    • Dimensionality reduction
    • Face recognition
    • Manifold
    • Nonlinear PCA
    • Self-organizing map


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