Piecewise one dimensional Self Organizing Map for fast feature extraction

It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensionality reduction are unable to solve this problem smoothly, which makes feature extraction task much difficult. As such, a novel method performs feature extraction and dimensionality reduction for high-dimensional data is needed. In this paper, a new algorithm for traditional Self Organizing Map (SOM) is presented to cope with this problem with low computation cost. It is shown here that the computation cost of the proposed approach, comparing to traditional SOM is reduced into O(d₁+ d₂ +...+ d_N) instead of O(d₁ × d₂ ×... × d_N), where d_j is the number of neurons through a dimension d_j of the feature map. Experiments are carried out using benchmark database show that the proposed algorithm is a good alternate to traditional SOM, especially, when high-dimensional feature space is desired.