The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Face recognition has become one of the most important research areas of pattern recognition and machine learning due to its potential applications in many fields. To effectively cope with this problem, a novel face recognition algorithm is proposed by using manifold learning and minimax probability machine. Comprehensive comparisons and extensive experiments show that the proposed algorithm achieves...
Neighborhood Preserving Embedding (NPE) is a subspace learning algorithm, which has the ability of preserving local neighborhood structure on the data manifold. Though NPE has been applied in many domains of pattern recognition, it is a vector-based method and will be encountered the small size sample (SSS) problem when it is directly applied to face recognition. To address this problem, the popular...
In this paper , we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high-speed feature extraction method, the present algorithm in this paper is the improved version of Simple-FLDA...
This paper presents a pattern classification system in which feature extraction and classifier learning are simultaneously carried out not only online but also in one pass where training samples are presented only once. For this purpose, we have extended incremental principal component analysis (IPCA) and some classifier models were effectively combined with it. However, there was a drawback in this...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.