Consistent feature selection and its application to face recognition
| Autor: | Feng Pan, Xiaobing Gan, Qiwei Gu, Guangwei Song |
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| Rok vydání: | 2014 |
| Předmět: |
Similarity (geometry)
Computer Networks and Communications Computer science business.industry Feature vector Pattern recognition Feature selection computer.software_genre Facial recognition system ComputingMethodologies_PATTERNRECOGNITION Data point Artificial Intelligence Hardware and Architecture Feature (computer vision) Pattern recognition (psychology) Artificial intelligence Data mining Laplacian matrix business computer Software Information Systems |
| Zdroj: | Journal of Intelligent Information Systems. 43:307-321 |
| ISSN: | 1573-7675 0925-9902 |
| DOI: | 10.1007/s10844-014-0324-5 |
| Popis: | In this paper we consider feature selection for face recognition using both labeled and unlabeled data. We introduce the weighted feature space in which the global separability between different classes is maximized and the local similarity of the neighboring data points is preserved. By integrating the global and local structures, a general optimization framework is formulated. We propose a simple solution to this problem, avoiding the matrix eigen-decomposition procedure which is often computationally expensive. Experimental results demonstrate the efficacy of our approach and confirm that utilizing labeled and unlabeled data together does help feature selection with small number of labeled samples. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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