The aim of standardization techniques is to replace the values of an attribute with values obtained through an appropriate transformation. However, there are situations in which more complex transformations are used to generate new attributes that represent a set of additional columns in the matrix X representing the dataset D. Transformations of this kind are usually referred to as feature extraction. For example, suppose that a set of attributes indicate the spending of each customer over consecutive time intervals. It is then possible to define new variables capable of capturing the trends in the data through differences or ratios between spending amounts of contiguous periods.
In other instances, the transformations may take even more complex forms, such as Fourier transforms, wavelets, and kernel functions. The use of such methods will be explained within the classification methods called support vector machines.
Attribute extraction may also consist of the creation of new variables that summarize within themselves the relevant information contained in a subset of the original attributes. For example, in the context of image recognition, one is often interested in identifying the existence of a face within a digitalized photograph. There are different indicators intended for the synthesis of each piece of information contained in a group of adjacent pixels, which make it easier for classification algorithms to detect faces.
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