We propose methods for outlier handling and noise reduction using weighted local linear smoothing for a set of noisy points sampled from a nonlinear manifold. Weighted PCA is used as a building block for our methods and we suggest an iterative weight selection scheme for robust local linear fitting together with an outlier detection method based on minimal spanning trees to further improve robustness. We also develop an efficient and effective bias-reduction method to deal with the "trim the peak and fill the valley" phenomenon in local linear smoothing. Synthetic examples along with several image data sets are presented to show that manifold learning methods combined with weighted local linear smoothing give more accurate results.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Local smoothing for manifold learning


    Contributors:


    Publication date :

    2004-01-01


    Size :

    1962865 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Local Smoothing for Manifold Learning

    Park, J. / Zhang, Z. / Zha, H. et al. | British Library Conference Proceedings | 2004


    Local Manifold Matching for Face Recognition

    Liu, W. / Fan, W. / Wang, Y. et al. | British Library Conference Proceedings | 2005


    Local manifold matching for face recognition

    Wei Liu, / Wei Fan, / Yunhong Wang, et al. | IEEE | 2005


    Acceleration smoothing method based on learning

    ZHANG XIAOFENG / JIANG DI | European Patent Office | 2023

    Free access

    Aeroengine Prognostics via Local Linear Smoothing, Filtering and Prediction

    Ariyur, K. B. / Jelinek, J. / Society of Automotive Engineers | British Library Conference Proceedings | 2004