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.
We explore sparse regression for effective feature selection and classification in face identity and expression recognition. We argue that sparse regression in pixel space is inappropriate. We propose instead a method which combines the virtues of sparse regression with projection methods such as PCA and FDA. The method can learn a sparse set of discriminative projections and increase recognition...
Diffusion Maps (DiffMaps) has recently provided a general framework that unites many other spectral manifold learning algorithms, including Laplacian Eigenmaps, and it has become one of the most successful and popular frameworks for manifold learning to date. However, Diffusion Maps still often creates unnecessary distortions, and its performance varies widely in response to parameter value changes...
As an alternative to standard PCA, matrix-based image dimensionality reduction methods have recently been proposed and have gained attention due to reported computational efficiency and robust performance in classification. We unify all of these methods through one concept: Separable Principle Component Analysis (SPCA).We show that the proposed matrix methods are either equivalent to, special cases...
We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost. The algorithm can be viewed as a coordinate descent method for the l1-regularized...
We examine the computation of information-theoretic image registration metrics and propose two (deterministic and stochastic) nonuniform subsampling methods for improving the efficiency. The proposed schemes attempt to use only the most relevant information as the basis of the computation. Both methods are shown to yield considerable improvement over the current practice of uniform subsampling. Theoretical...
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.