Unsupervised classification, supervised

Pattern recognition can be classified according to several parameters. Below we discuss only the supervised/unsupervised dichotomy because it represents two different ways of analyzing hyperspectral data cubes. Unsupervised methods (cluster analysis) classify image pixels without calibration and with spectra only, in contrast to supervised classifications. Feature extraction methods [21] such as PCA or wavelet compression are often applied before cluster analysis. 

Classification, or the division of data into groups, methods can be broadly of two types supervised and unsupervised. The primary difference is that prior information about classes into which the data fall is known and representative samples from these classes are available for supervised methods. The supervised and unsupervised approaches loosely lend themselves into problems that have prior hypotheses and those in which discovery of the classes of data may be needed, respectively. The division is purely for organization purposes in many applications, a combination of both methods can be very powerful. In general, biomedical data analysis will require multiple spectral features and will have stochastic variations. Hence, the field of statistical pattern recognition [88] is of primary importance and we use the term recognition with our learning and classification method descriptions below. 

Focus on Spectra Statistical Testing Supervised Statistical Classification Unsupervised Statistical Classification... 

In chemometrics we are very often dealing not with individual signals, but with sets of signals. Sets of signals are used for calibration purposes, to solve supervised and unsupervised classification problems, in mixture analysis etc. All these chemometrical techniques require uniform data presentation. The data must be organized in a matrix, i.e. for the different objects the same variables have to be considered or, in other words, each object is characterized by a signal (e.g. a spectrum). Only if all the objects are represented in the same parameter space, it is possible to apply chemometrics techniques to compare them. Consider for instance two samples of mineral water. If for one of them, the calcium and sulphate concentrations are measured, but for the second one, the pH values and the PAH s concentrations are available, there is no way of comparing these two samples. This comparison can only be done in the case, when for both samples the same sets of measurements are performed, e.g. for both samples, the pH values, and the calcium and sulphate concentrations are determined. Only in that case, each sample can be represented as a point in the three-dimensional parameter space and their mutual distances can be considered measures of similarity. 

Scale dendrograms can in principle be applied to both unsupervised and supervised classification, however in this chapter only examples from unsupervised classification are included. 

Cluster analysis. In this section, the application of the simple multiscale approach to cluster analysis is demonstrated. The masking method will also be used to localise important features. There are several possible cluster analysis algorithms, however only discriminant function analysis (DFA) will be used here. Before discussing the results from the simple multiscale analysis, this section will first present DFA and how it can applied to both unsupervised and supervised classification, followed by how the cluster properties S are measured at each resolution level. 

The unsupervised classification is too much of a generalization and the clusters only roughly match some of the actual classes. Its value is mainly as a guide to the spectral content of a scene to aid in making a preliminary interpretation prior to conducting the much more powerful supervised classification procedures. There are several types of unsupervised classification which are explained briefly below ... 

AI research has already provided the concepts of supervised and unsupervised learning to data analysis, and these have proved useful in the classification of analytical methods and to alert us to the potential danger of chance effects. But what of the application of AI techniques themselves... 

In terms of chemometrics, the concepts presented here fall into the category of supervised classification methods, because it must be known beforehand which group each specimen falls into. Unsupervised training methods also exist, in which samples are grouped based solely on the characteristics of the data. In order to use the methods presented here, the additional information, in other words, which samples belong to which group, must be known beforehand. It is conceivable, however, that that information could be derived from an unsupervised classification scheme and then applied to the supervised scheme. 

Because all metabolites cannot routinely be identified and quantified in a complex metabolome it is often satisfactory to investigate patterns of the metabolome to determine changes due to external stress on the biosystem. Data from metabolome analysis are complex and large. Thus multivariant analyses are often used to provide meaningful data. There are two types of multivariant analysis approaches used to statistically analyze metabolic data supervised and unsupervised methods. As shown above in the volatile breath analysis by IMS, discriminant analysis was used to determine healthy patients from patients suffering from lung cancer. Discriminant analysis is a supervised method, meaning the classification of the sample must be... 

The most frequently used supervised pattern recognition method is the linear discriminant analysis (LDA), not to be confused with its twin brother canonical correlation analysis (CCA) or canonical variate analysis (CVA). Recently, classification and regression trees (CART) produced surprisingly good results. Artificial neural networks (ANNs) can be applied for both prediction and pattern recognition (supervised and unsupervised). 

Supervised and unsupervised classification for example PCA, K-means and fuzzy clustering, linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), fisher discriminant analysis (FDA), artificial neural networks (ANN). 

SVM/SVMR support vectors machine-regression—Unsupervised pattern recognition, supervised classification, quantization WLS weighted least squares—Spectral baseline correction... 

S. Momon, N. Godin, P. Reynaud, M. R mili, And G. Fantozzi, Unsupervised and supervised classification of AE data collected during fatigue test on CMC at high temperature. Composites Part A-Applied Science and Manufacturing, 2011. 

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