It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn ...
Given a set of points S = fp1; : : : ; png in Euclidean d-dimensional space, we address the problem of computing the d-dimensional annulus of smallest width containing the set. We...
We present a framework for computing optimal transformations, aligning one point set to another, in the presence of outliers. Example applications include shape matching and regis...
—A novel framework is proposed for the design of cost-sensitive boosting algorithms. The framework is based on the identification of two necessary conditions for optimal cost-sen...
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...