Discovering Highly Informative Feature Set Over High Dimensions - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

Discovering Highly Informative Feature Set Over High Dimensions

Résumé

For 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.
Fichier principal
Vignette du fichier
ictai12.pdf (150.29 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

lirmm-00753807 , version 1 (19-11-2012)

Identifiants

Citer

Chongsheng Zhang, Florent Masseglia, Xiangliang Zhang. Discovering Highly Informative Feature Set Over High Dimensions. ICTAI: International Conference on Tools with Artificial Intelligence, Nov 2012, Athens, Greece. pp.1059-1064, ⟨10.1109/ICTAI.2012.149⟩. ⟨lirmm-00753807⟩
311 Consultations
440 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More