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dc.contributor.authorZhang, Chongsheng
dc.contributor.authorMasseglia, Florent
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2015-08-04T07:05:25Z
dc.date.available2015-08-04T07:05:25Z
dc.date.issued2012-11
dc.identifier.citationChongsheng Zhang, Masseglia, F., & Xiangliang Zhang. (2012). Discovering Highly Informative Feature Set over High Dimensions. 2012 IEEE 24th International Conference on Tools with Artificial Intelligence. doi:10.1109/ictai.2012.149
dc.identifier.isbn9780769549156
dc.identifier.issn10823409
dc.identifier.doi10.1109/ICTAI.2012.149
dc.identifier.urihttp://hdl.handle.net/10754/564625
dc.description.abstractFor many textual collections, the number of features is often overly large. These features can be very redundant, it is therefore desirable to have a small, succinct, yet highly informative collection of features that describes the key characteristics of a dataset. Information theory is one such tool for us to obtain this feature collection. With this paper, we mainly contribute to the improvement of efficiency for the process of selecting the most informative feature set over high-dimensional unlabeled data. We propose a heuristic theory for informative feature set selection from high dimensional data. Moreover, we design data structures that enable us to compute the entropies of the candidate feature sets efficiently. We also develop a simple pruning strategy that eliminates the hopeless candidates at each forward selection step. We test our method through experiments on real-world data sets, showing that our proposal is very efficient. © 2012 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectFeature Selection
dc.subjecthigh dimensions
dc.subjectUnsupervised
dc.titleDiscovering highly informative feature set over high dimensions
dc.typeConference Paper
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journal2012 IEEE 24th International Conference on Tools with Artificial Intelligence
dc.conference.date7 November 2012 through 9 November 2012
dc.conference.name2012 IEEE 24th International Conference on Tools with Artificial Intelligence, ICTAI 2012
dc.conference.locationAthens
dc.contributor.institutionHenan University, 475004 Kaifeng, China
dc.contributor.institutionZenith Team, INRIA, 34095 Montpellier, France
kaust.personZhang, Xiangliang


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