Using Convex Sets for Exploratory Data Analysis and Visualization |
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Authors: | Wojciech Grohman |
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Affiliation: | (1) WaveCrest Labs, USA |
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Abstract: | In this paper a new, abstract method for analysis and visualization of multidimensional data sets in pattern recognition problems
is introduced. It can be used to determine the properties of an unknown, complex data set and to assist in finding the most
appropriate recognition algorithm. Additionally, it can be employed to design layers of a feedforward artificial neural network
or to visualize the higher-dimensional problems in 2-D and 3-D without losing relevant data set information. The method is
derived from the convex set theory and works by considering convex subsets within the data and analyzing their respective
positions in the original dimension. Its ability to describe certain set features that cannot be explicitly projected into
lower dimensions sets it apart from many other visualization techniques. Two classical multidimensional problems are analyzed
and the results show the usefulness of the presented method and underline its strengths and weaknesses. |
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Keywords: | data visualization pattern recognition pattern classification neural networks |
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