A conditional independence perspective of variable selection

Sohan Seth, José C Principe

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Variable selection is a necessary preprocessing stage in many applications, such as regression and classification, to reduce computational cost, to avoid curse of dimensionality and to improve generalization. A filter type approach to variable selection employs statistical criteria such as dependence to quantify the importance of a variable. In this paper we discuss the use of conditional independence as a criteria for variable selection, and describe a forward selection and a backward elimination based approach using this notion. We introduce two measures of conditional independence, describe their respective estimators and apply them in the variable selection task. We also provide a brief overview of the available variable selection methods and compare the proposed methods with these methods.
Original languageEnglish
Title of host publicationMachine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on
Number of pages6
Publication statusPublished - 29 Aug 2010


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