Recently, the problem of signal representation in terms of basis vectors from a large, ”overcomplete”, spanning dictionary has been the focus of much research. Achieving a succinct, or ”sparse”, representation is known as the problem of best basis representation. We consider methods which seek to solve this problem by sequentially building up a basis set for the signal. Three distinct algorithm...

Both classical Forward Selection and the more modern Lasso provide computationally feasible methods for performing variable selection in high dimensional regression problems involving many predictors. We note that although the Lasso is the solution to an optimization problem while Forward Selection is purely algorithmic, the two methods turn out to operate in surprisingly similar fashions. Our ...

breast cancer is the most common type of cancer among women. the important key to treat the breast cancer is early detection of it because according to many pathological studies more than 80% of all abnormalities are still benign at primary stages; so in recent years, many studies and extensive research done to early detection of breast cancer with higher precision and accuracy.infra-red breast...

Subset selection and regularisation are two well known techniques which can improve the generalisation performance of nonparametric linear regression estimators, such as radial basis function networks. This paper examines regularised forward selection (RFS) { a combination of forward subset selection and zero-order regularisation. An eecient implementation of RFS into which either delete-1 or g...

We introduce a new approach for learning part-based object detection through feature synthesis. Our method consists of an iterative process of feature generation and pruning. A feature generation procedure is presented in which basic part-based features are developed into a feature hierarchy using operators for part localization, part refining and part combination. Feature pruning is done using...

A common problem in many model-building situations is to choose from a large set of covariates that should be included in the “best” model. An additional consideration in modeling epidemiological data is the inclusion of confounders, which adds a quirk in the modeling procedure in that statistical significance is not the main criteria for keeping predictors in a model. Hosmer and Lemeshow (2000...