Automatic induction of projection pursuit indices

IEEE Transactions on Neural Networks
E Rodriguez-MartinezJason F Ralph

Abstract

Projection techniques are frequently used as the principal means for the implementation of feature extraction and dimensionality reduction for machine learning applications. A well established and broad class of such projection techniques is the projection pursuit (PP). Its core design parameter is a projection index, which is the driving force in obtaining the transformation function via optimization, and represents in an explicit or implicit way the user's perception of the useful information contained within the datasets. This paper seeks to address the problem related to the design of PP index functions for the linear feature extraction case. We achieve this using an evolutionary search framework, capable of building new indices to fit the properties of the available datasets. The high expressive power of this framework is sustained by a rich set of function primitives. The performance of several PP indices previously proposed by human experts is compared with these automatically generated indices for the task of classification, and results show a decrease in the classification errors.

References

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Citations

May 8, 2014·IEEE Transactions on Cybernetics·E Rodriguez-MartinezJ Y Goulermas
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Eduardo Rodriguez-MartinezJohn Yannis Goulermas
Aug 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Tingting MuJohn Y Goulermas
Oct 27, 2011·IEEE Transactions on Neural Networks·Yu SuWen Gao
Feb 26, 2011·IEEE Transactions on Neural Networks·Valero LaparraJesús Malo
Jun 24, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Tingting MuSophia Ananiadou

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