Pattern recognition soft independent modeling

Classical supervised pattern recognition methods include /( -nearest neighbor (KNN) and soft independent modeling of class analogies (SIMCA). Both... 

Application of Soft Independent Modeling of Class Analogy Pattern Recognition to Air Pollutant Analytical Data... 

Current methods for supervised pattern recognition are numerous. Typical linear methods are linear discriminant analysis (LDA) based on distance calculation, soft independent modeling of class analogy (SIMCA), which emphasizes similarities within a class, and PLS discriminant analysis (PLS-DA), which performs regression between spectra and class memberships. More advanced methods are based on nonlinear techniques, such as neural networks. Parametric versus nonparametric computations is a further distinction. In parametric techniques such as LDA, statistical parameters of normal sample distribution are used in the decision rules. Such restrictions do not influence nonparametric methods such as SIMCA, which perform more efficiently on NIR data collections. 

If the membership of objects to particular clusters is known in advance, the methods of supervised pattern recognition can be used. In this section, the following methods are explained linear learning machine (LLM), discriminant analysis, A -NN, the soft independent modeling of class analogies (SIMCA) method, and Support Vector Machines (SVMs). 

Acoustic emission power spectra are similar in many respects to optical spectra and are amenable to chemometric processing (multivariate analysis). Principal component analysis, partial least squares (PLS), neural networks, and qualitative techniques such as SIMCA (soft independent modeling of class analogy a pattern recognition technique) have been employed... 

Fig. 1. Pattern recognition methods. ANN, artificial neural networks BP ANN, back-propagation ANN CA, cluster analysis CART, classification and regression trees (recursive partitioning) CCA, canonical correlation analysis CVA, canonical variate analysis kNN, -nearest neighbor methods LDA, linear discriminant analysis PCA, principal component analysis PLS DA, partial least squares regression discriminant analysis SIMCA, soft independent modeling of class analogy SOM, self-organizing maps.

One of the most popular pattern recognition methods in chemistry is SIMCA, an acronym for soft/simple independent modeling of class analogy. The central idea is to represent each class of objects by a separate principal component model. Because a probability can be estimated for belonging to a certain class and because outliers can be detected the method is called soft. Classification methods such as discriminant analysis are called hard if they give a categorical answer about the class membership. 

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