Pattern recognition unsupervised techniques

Fig. 3. Types of pattern recognition techniques (a) preprocessing, (b) display, (c) unsupervised learning, and (d) supervisediearning.

Analytical results are often represented in a data table, e.g., a table of the fatty acid compositions of a set of olive oils. Such a table is called a two-way multivariate data table. Because some olive oils may originate from the same region and others from a different one, the complete table has to be studied as a whole instead as a collection of individual samples, i.e., the results of each sample are interpreted in the context of the results obtained for the other samples. For example, one may ask for natural groupings of the samples in clusters with a common property, namely a similar fatty acid composition. This is the objective of cluster analysis (Chapter 30), which is one of the techniques of unsupervised pattern recognition. The results of the clustering do not depend on the way the results have been arranged in the table, i.e., the order of the objects (rows) or the order of the fatty acids (columns). In fact, the order of the variables or objects has no particular meaning. 

D. Coomans and D.L. Massart, Potential methods in pattern recognition. Part 2. CLUPOT an unsupervised pattern recognition technique. Anal. Chim. Acta., 133 (1981) 225-239. 

While principal components models are used mostly in an unsupervised or exploratory mode, models based on canonical variates are often applied in a supervisory way for the prediction of biological activities from chemical, physicochemical or other biological parameters. In this section we discuss briefly the methods of linear discriminant analysis (LDA) and canonical correlation analysis (CCA). Although there has been an early awareness of these methods in QSAR [7,50], they have not been widely accepted. More recently they have been superseded by the successful introduction of partial least squares analysis (PLS) in QSAR. Nevertheless, the early pattern recognition techniques have prepared the minds for the introduction of modem chemometric approaches. 

The two pattern recognition techniques used In this work are among those usually used for unsupervised learning. The results will be examined for the clusters which arise from the analysis of the data. On the other hand, the number of classes and a rule for assigning compounds to each had already been determined by the requirements of the mixture analysis problem. One might suppose that a supervised approach would be more suitable. In our case, this Is not so because our aim Is not to develop a classifier. Instead, we wish to examine the data base of FTIR spectra and the metric to see If they are adequate to help solve a more difficult problem, that of analyzing complex mixtures by class. 

Hierarchical cluster analysis (HCA) is an unsupervised technique that examines the inteipoint distances between all of the samples and represents that information in the form of a twcKlimensional plot called a dendrogram. These dendrograms present the data from high-dimensional row spaces in a form that facilitates the use of human pattern-recognition abilities. 

The goal of unsupervised techniques is to identify and display natural groupings in the data without imposing any prior class membership. Even when the ultimate goal of the project is to develop a supervised pattern recognition model, we recommend the use of unsupervised techniques to provide an initial view of the data. 

Fig. 10.11 Representation of hybrid approach in which Kohonen maps, neural nets, multiple component analysis, and pattern recognition are combined to create a complex data evaluation cascade. Within this cascade supervised (quantitative) and unsupervised (qualitative) techniques are combined (Hierlemann et al., 1996)...

Table 7.3 Some of the More Common Pattern Recognition Techniques Supervised Learning Unsupervised Learning...

Pattern Recognition. The application of computers to build descriptive or predictive models (i.e., find patterns) of information from input datasets. The techniques of pattern recognition overlap those used in statistics, chemometrics, and data mining, and include data display, description, and reduction, unsupervised methods such as cluster analy-... 

Progress in chemometrics has made a number of new statistical techniques available, which are increasingly being used. This concerns both new supervised and unsupervised (or pattern recognition ) techniques. Chemometrics was dehned about 25 years ago as the chemical discipline which uses mathematical, statistical and related techniques to design optimal measurement procedures and experiments, and to extract maximum relevant information from chemical data. The science of chemometrics has been developed to promote applications of statistics in analytical, organic and medicinal chemistry. 

Pattern recognition techniques can be divided into display, preprocessing, supervised, and unsupervised learning. Pattern recognition methods are used among others in the search for correlations between sequence, structure, and biological activity in cheminformatics and bioinformatics. 

After applying the appropriate pre-processing, different chemometric techniques can be applied according to the aim of the study. Pattern recognition is one of the chemometric methods most used in analytical chemistry and this is true for separations data. Pattern recognition can be generally divided into two classes exploratory data analysis and unsupervised and supervised pattern recognition (Otto, 2007 Brereton, 2007). 

PCA involves a mathematical procedure that transforms a number of correlated variables into a smaller number of uncorrelated variables called principal components (PCs). PCA can reduce the dimensionality of multidimensional space while yet retaining a large amount of the original information in the data. For example, two-dimensional data may be transformed into one-dimensional data, as shown in Figure 11.3. The first PC accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. Moreover, PCA is one of the unsupervised pattern recognition techniques, and therefore provides results unbiased by human input. 

Ann X m matrix can be considered n points in the m-dimensional space (or m points in the n-dimensional space). The points can be projected into a smaller dimensional subspace (smaller than n or m, whichever is the smaller) using proper techniques as PCA. Therefore, PCA is often called as a projection method. Projecting the points, dimension reduction of the data can be achieved. The principal components are often called underlying components their values are the scores. The principal components are, in fact, linear combinations of the original variables. PCA is an unsupervised method of pattern recognition in the sense that no grouping of the data has to be known before the analysis. Still the data structure can be revealed easily and class membership is easy to assiga... 

Two types of pattern recognition technique " have been applied to spectrum interpretation. Supervised methods (see Supervised Learning) are limited to one or more predefined structural classes and require representative training sets for each to develop the classifier. Unsupervised methods (see Unsupervised Learning) partition a set of spectra into clusters with common structural features on the basis of spectral features alone. No predefined classes are required. 

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