We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can ...
A new spectral approach to value function approximation has recently been proposed to automatically construct basis functions from samples. Global basis functions called proto-val...
This paper addresses the problem of approximate singular value decomposition of large dense matrices that arises naturally in many machine learning applications. We discuss two re...
Abstract. This paper presents a new spectrally-based single image relighting approach. We first compute the spectral radiance of each stimulus on the monitor when displaying the i...
—A new formulation for multiway spectral clustering is proposed. This method corresponds to a weighted kernel principal component analysis (PCA) approach based on primal-dual lea...