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.
Based on UDP and MFA, we propose a new unsupervised feature extraction algorithm, LMP (Local Marginal Projection), which is built on local quality. It measures the non-local quantities by the nearest sample between two locals. The goal of LMP is to find a projection that can maximize the distance of the sample in the same local and in different locals, in which case, the data can be projected into...
A new feature extraction method based on manifold learning is proposed for face recognition in the paper; its criterion function is characterized by maximizing the difference between the nonlocal scatter and the local scatter. The novel method is called two-directional two-dimensional marginal discriminant projection ((2D)2MDP), which simultaneously works image matrix in the row direction and in the...
Based on manifold learning, a new feature extraction method is proposed for face recognition in the paper. The new method is called two-directional two-dimensional unsupervised discriminant projection ((2D)2UDP), which simultaneously works image matrix in the row direction and in the column direction for feature extraction. The experimental results on ORL face databases and AR face databases indicate...
This paper proposes a two-phase algorithm of image projection discriminant analysis. The new discriminant method is composed of feature extraction by on maximum margin criterion (MMC) and Fisher discriminant analysis (FDA). The algorithm includes two stages: firstly, the feature extraction based on maximum margin criterion (MMC) is employed to condense the dimension of image matrix; Then Fisher discriminant...
A improved method of feature extraction based on kernel maximum margin criterion (KMMC) is presented for face recognition in this paper, i.e. a simple algorithm of uncorrected optimal discriminant vectors in kernel feature space is proposed for nonlinear feature extraction. The proposed method has more powerful capability to eliminate the statistical correlation between feature vectors and its mathematical...
For nonlinear feature extraction, a new feature extraction method based on kernel maximum margin criterion (KMMC) is presented in this paper, i.e., an algorithm of statistically uncorrelated optimal discriminant vectors in kernel feature space is proposed in the paper. The proposed method has more powerful capability to eliminate the statistical correlation between features and improve efficiency...
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.