Abstract
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.
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Huang, W., Yin, H. (2009). ViSOM for Dimensionality Reduction in Face Recognition. In: PrÃncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_13
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DOI: https://doi.org/10.1007/978-3-642-02397-2_13
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