On node selection for classification in correlated data sets

R. Cristescu

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    Consider a system which can be in a finite number of states. Given a large number of characteristics which are measured, representing the system, we are concerned with the selection of a subset of characteristics of (small) given cardinality, for which the classification of the system according to one of the states in the state set is optimal according to the Rayleigh quotient criterion. This problem is relevant in various scenarios where a few explanatory variables have to be selected from a large set of candidates, including sensor selection in sensor networks, classification in image processing, and feature selection in data mining for bioinformatics applications. We show that the optimization amounts to finding the submatrix of the features covariance matrix for which the sum of elements of the inverse is maximized, and we present bounds which relate this optimization to a similar metric based on elements of the original covariance matrix.
    Original languageEnglish
    Title of host publication42nd Annual Conference on Proceedings of Information Sciences and Systems, 2008. CISS 2008
    Place of PublicationPiscataway
    PublisherInstitute of Electrical and Electronics Engineers
    Pages1064-1068
    ISBN (Print)978-1-4244-2246-3
    DOIs
    Publication statusPublished - 2008
    Event42nd Annual Conference on Information Sciences and Systems (CISS 2008), March 19-21, 2008, Princeton, NJ, USA - Princeton, NJ, United States
    Duration: 19 Mar 200821 Mar 2008

    Conference

    Conference42nd Annual Conference on Information Sciences and Systems (CISS 2008), March 19-21, 2008, Princeton, NJ, USA
    Abbreviated titleCISS 2008
    CountryUnited States
    CityPrinceton, NJ
    Period19/03/0821/03/08

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