Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. While naturally cast as a combinatorial optimization problem, variable or feature selection admits a convex relaxation through the regularization by the l1-norm. In this paper, we consider situations where we are not only interested in sparsity, but where some structural prior knowledge is ...

We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. By avoiding explicit principal component analysis (PCA), our algorithm is the first with no runtime dependen...

University of Minnesota, USA 1.

a procedure for an efficient recovering of reflectance spectra of acrylic paint samples from cie tristimulus color values is described. by fixing a certain criteria based on color difference value, the proposed technique preliminarily selects a series of suitable samples from a main dataset containing the reflectance values of a series of different acrylic paint samples, based on the color spec...

medicinal plant species constitute a considerable part of the flora in iran and play a major role in the composition of plant communities. therefore, it is necessary to recognize the factors leading to the establishment and distribution of vegetation. for data sampling (2012), land units were specified. the plot size was determined using minimal area method and number of plots was determined by...

The spaceborne synthetic aperture radar (SAR) sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form equivalent aperture, and achieve third dimensionality recognition. In this paper, combined with actual triple star orbits, a SAR method based on spectrum interferometry principal component analysis (PCA) presented. Firstly, interferome...