ViSOM for dimensionality reduction in face recognition

Weilin Huang, Hujun Yin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    The self-organizing map (SOM) is a classical neural network method for dimensionality reduction and data visualization. Visualization induced SOM (ViSOM) and growing ViSOM (gViSOM) are two recently proposed variants for a more faithful, metric-based and direct data representation. They learn local quantitative distances of data by regularizing the inter-neuron contraction force while capturing the topology and minimizing the quantization error. In this paper we first review related dimension reduction methods, and then examine their capabilities for face recognition. The experiments were conducted on the ORL face database and the results show that both ViSOM and gViSOM significantly outperform SOM, PCA and related methods in terms of recognition error rate. In the training with five faces, the error rate of gViSOM dimension reduction followed by a soft k-NN classifier reaches as low as 2.1%, making ViSOM an efficient approach for data representation and dimensionality reduction. © 2009 Springer-Verlag Berlin Heidelberg.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    Number of pages8
    Publication statusPublished - 2009
    Event7th International Workshop on Self-Organizing Maps, WSOM 2009 - St. Augustine, FL
    Duration: 1 Jul 2009 → …


    Conference7th International Workshop on Self-Organizing Maps, WSOM 2009
    CitySt. Augustine, FL
    Period1/07/09 → …


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