Pattern Discovery Through Separable Data Projections
Data projections or, more generally, data linear transformations, in some cases allow to enhance interesting regularities in data sets. We pay particular attention to linear transformations from multidimensional feature space on a line and on a plane. In such cases, transformed data sets can be visualized and the resulting patterns can be evaluated by an expert both analytically and subjectively in accordance with the expert’s opinion. The projection pursuit provides well developed methods for designing interesting projections of data sets related to the normal model. Here we are considering separability criteria for designing projections.
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