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Feature extractions for small sample size classification problem / B.C. Kuo in IEEE Transactions on geoscience and remote sensing, vol 45 n° 3 (March 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 3 (March 2007) . - pp 756 - 764 Titre : Feature extractions for small sample size classification problem Type de document : Article/Communication Auteurs : B.C. Kuo, Auteur ; K.Y. Chang, Auteur Année de publication : 2007 Article en page(s) : pp 756 - 764 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme génétique [Termes IGN] classification dirigée [Termes IGN] décomposition du pixel [Termes IGN] détection de contours [Termes IGN] reconnaissance de formes [Termes IGN] valeur propre Résumé : (Auteur) Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decomposition method, and a new regularization technique was proposed. In the hyperspectral image experiment, the effects of these three components of feature extraction are explored on ill-posed and poorly posed conditions. The experimental results show that different regularization methods need to cooperate with different eigenvalue decomposition methods to reach the best performance, the proposed regularization method, regularized feature extraction (RFE) outperform others, and the best feature extraction for a small sample size classification problem is RFE with nonparametric weighted scatter matrices. Numéro de notice : A2007-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.885074 En ligne : https://doi.org/10.1109/TGRS.2006.885074 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28453 [article]

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