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.
One of the challenges the face recognition application is facing today is that of the high dimensionality of multivariate data. In this context, this paper proposes to compare the performance of a triumvirate combination of linear dimensionality reduction techniques namely Singular Value Decomposition (SVD) which maximizes the variance of the training vectors, Direct Fractional Linear Discriminant...
Principal component analysis (PCA) has long been a simple, efficient technique for dimensionality reduction. However, many nonlinear methods such as local linear embedding and curvilinear component analysis have been proposed for increasingly complex nonlinear data recently. In this paper, we investigate and compare linear PCA and various nonlinear methods for face recognition. Results drawn from...
Dimension reduction techniques have been successfully applied to face recognition and text information retrieval. The process can be time-consuming when the data set is large. This paper presents a multilevel framework to reduce the size of the data set, prior to performing dimension reduction. The algorithm exploits nearest-neighbor graphs. It recursively coarsens the data by finding a maximal matching...
Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images. Firstly, this paper proposes an algorithm named two-dimensional...
Dimensionality reduction is usually involved in the domains of artificial intelligence and machine learning. Linear projection of features is of particular interest for dimensionality reduction since it is simple to calculate and analytically analyze. In this paper, we propose an essentially linear projection technique, called locality-preserved maximum information projection (LPMIP), to identify...
Numerous face recognition algorithms use principal component analysis (PCA) as the first step for dimensionality reduction (DR) followed by linear discriminant analysis (LDA). PCA is applied in the beginning because it performs the DR in the minimum square error sense and achieves the most compact representation of data. However, they lack discrimination ability. To optimize classification, LDA and...
In this paper, we propose the use of (adaptive) nonlinear approximation for dimensionality reduction. In particular, we propose a dimensionality reduction method for learning a parts based representation of signals using redundant dictionaries. A redundant dictionary is an overcomplete set of basis vectors that spans the signal space. The signals are jointly represented in a common subspace extracted...
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.