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11

ICML
2000
IEEE

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Mutual Information in Learning Feature Transformations

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Mutual Information in Learning Feature Transformations

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We present feature transformations useful for exploratory data analysis or for pattern recognition. Transformations are learned from example data sets by maximizing the mutual information between transformed data and their class labels. We make use of Renyi’s quadratic entropy, and we extend the work of Principe et al. to mutual information between continuous multidimensional variables and discrete-valued class labels.

Kari Torkkola, William M. Campbell

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Class Labels | Exploratory Data Analysis | ICML 2000 | Machine Learning | Mutual Information |

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Added	01 Aug 2010
Updated	01 Aug 2010
Type	Conference
Year	2000
Where	ICML
Authors	Kari Torkkola, William M. Campbell

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