Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2011.020405
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 2 Issue 4, 2011.
Abstract: A self-organizing map (SOM) is a classical neural network method for dimensionality reduction. It comes under the unsupervised class. SOM is a neural network that is trained using unsupervised learning to produce a low-dimensional, discretized representation of the input space of the training samples, called a map. SOM uses a neighborhood function to preserve the topological properties of the input space. SOM operates in two modes: training and mapping. Using the input examples, training builds the map. It is also called as vector quantization. In this paper, we first survey related dimension reduction methods and then examine their capabilities for face recognition. In this work, different dimensionality reduction techniques such as Principal component analysis [PCA], independent component analysis [ICA] and self-organizing map [SOM] are selected and applied in order to reduce the loss of classification performance due to changes in facial expression. The experiments were conducted on ORL face database and the results show that SOM is a better technique.
Shamla Mantri, Nikhil S. Tarale and Sudip C. Mahajan, “ Dimensionality Reduction technique using Neural Networks – A Survey” International Journal of Advanced Computer Science and Applications(IJACSA), 2(4), 2011. http://dx.doi.org/10.14569/IJACSA.2011.020405