SOM integrated with CCA for the feature map and classification of complex chemical patterns

Considering that the two-dimensional (2D) feature map of the high-dimensional chemical patterns can more concisely and efficiently represent the pattern characteristic, a new procedure integrating self-organizing map (SOM) networks with correlative component analysis (CCA) is proposed. Firstly, CCA was used to identify the most important classification characteristics (CCs) from the original high-dimensional chemical pattern information. Then, the SOM maps the first several CCs, which include the most useful information for pattern classification, onto a 2D plane, on which the pattern classification feature is concisely represented. To improve the learning efficiency of SOM networks, two new algorithms for dynamically adjusting the learning rate and the range of neighborhood around the winning unit were further worked out. Besides, a convenient method for detecting the topologic nature of SOM results was proposed. Finally, a typical example of mapping two classes natural spearmint essence was employed to verify the effectiveness of the new approach. The feature-topology-preserving (FTP) map obtained can well represent the classification of original patterns and is much better than what obtained by SOM alone.