Feature map

T. Kohonen, Self Organization and Associated Memory. Springer-Verlag, Heidelberg, 1989. W.J. Meissen, J.R.M. Smits, L.M.C. Buydens and G. Kateman, Using artificial neural networks for solving chemical problems. II. Kohonen self-organizing feature maps and Hopfield networks. Chemom. Intell. Lab. Syst., 23 (1994) 267-291. 

The objective of data analysis (or feature extraction) is to transform numeric inputs in such a way as to reject irrelevant information that can confuse the information of interest and to accentuate information that supports the feature mapping. This usually is accomplished by some form of numeric-numeric transformation in which the numeric input data are transformed into a set of numeric features. The numeric-numeric transformation makes use of a process model to map between the input and the output. 

Feature mapping (i.e., numeric-symbolic mapping) requires decision mechanisms that can distinguish between possible label classes. As shown in Fig. 5, widely used decision mechanisms include linear discriminant surfaces, local data cluster criteria, and simple decision limits. Depending on the nature of the features and the feature extraction approaches, one or more of these decision mechanisms can be selected to assign labels. 

Data Interpretation extends data analysis techniques to label assignment and considers both integrated approaches to feature extraction and feature mapping and approaches with explicit and separable extraction and mapping steps. The approaches in this section focus on those that form numeric-symbolic interpreters to map from numeric data to specific labels of interest. 

Numeric-symbolic approaches are particularly important in process applications because the time series of data is by far the dominant form of input data, and they are the methods of choice if annotated data exist to develop the interpretation system. With complete dependence on the annotated data to develop the feature mapping step, numeric-symbolic mappers can be used to assign labels directly. However, as the amount and coverage of available annotated data diminishes for the given label of interest, there is a need to integrate numeric-symbolic approaches with... 

All of the studies above have used back propagation multilayer perceptrons and many other varieties of neural network exist that have been applied to PyMS data. These include minimal neural networks,117119 radial basis functions,114120 self-organizing feature maps,110121 and autoassociative neural networks.122123... 

In contrast to common ANNs, Kohonen networks produce self-organized topological feature maps (Kohonen [1982, 1984]). The basic idea of Kohonen mapping is that information in data usually contains not only an algebraic but also a topological aspect. These double aspect is shown schematically in Fig. 8.25 where the data and the structure of them are composed. 

Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43 59... 

Also known as a Self-Organizing Feature Map or SOFM, or a Kohonen map after its inventor. 

Having selected the major features, the final stage of QSAR model building involves a feature mapping procedure. 

Comparative and phylogenetic considerations such as those outlined in the preceding paragraphs readily lead to speculation that the olfactory systems of modern animals share common antecedents and therefore probably also share common principles of functional organization and information processing. We might ask, What attributes of chemical stimuli do olfactory systems analyze and encode How are those features mapped in "neural space" at various levels of the olfactory pathway How are cells of the pathway organized, and what mechanisms do they use, to accomplish this analysis of odors in the environment ... 

Meissen, W. J., Smits, J. R. M., Rolf, G. H., Kateman, G. Chemom. Intell. Lab. Syst. 18,1993, 195-204. Two-dimensional mapping of IR spectra using a parallel implemented selforganizing feature map. 

The utility of ANNs as a pattern recognition technique in the field of microbeam analysis was demonstrated by Ro and Linton [99]. Back-propagation neural networks were applied to laser microprobe mass spectra (LAMMS) to determine interparticle variations in molecular components. Selforganizing feature maps (Kohonen neural networks) were employed to extract information on molecular distributions within environmental microparticles imaged in cross-section using SIMS. 

A. Samecka-Cymerman, A. Stankiewicz, K. Kolon and A. J. Kempers, Self-organizing feature map (neural networks) as a tool in classification of the relations between chemical composition of aquatic bryophytes and types of streambeds in the Tatra national park in Poland, Chemosphere, 67(5), 2007, 954-960. 

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