Learn More
Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained. Human decision making(More)
A variational level set method is developed for the supervised classification problem. Nonlinear classifier decision boundaries are obtained by minimizing an energy functional that is composed of an empirical risk term with a margin-based loss and a geometric regularization term new to machine learning: the surface area of the decision boundary. This(More)
Matrix factorization from a small number of observed entries has recently garnered much attention as the key ingredient of successful recommendation systems. One unresolved problem in this area is how to adapt current methods to handle changing user preferences over time. Recent proposals to address this issue are heuristic in nature and do not fully(More)
Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, must be quantized. Nearest neighbor and centroid conditions for quantizer optimality are derived using mean Bayes risk error as a distortion measure. An example of optimal quantization for hypothesis testing is provided. Human decision(More)
Satellite imagery is a form of big data that can be harnessed for many social good applications, especially those focusing on rural areas. In this article, we describe the common problem of selecting sites for and planning rural development activities as informed by remote sensing and satellite image analysis. Effective planning in poor rural areas benefits(More)
—For companies with large salesforces whose sellers approach business clients in teams, the problem of allocating sales teams to sales opportunities is a critical management task for maximizing the revenue and profit of the company. We approach this problem via predictive and prescriptive analytics, where the former involves data mining to learn the(More)
—Sparse signal representations and approximations from overcomplete dictionaries have become an invaluable tool recently. In this paper, we develop a new, heuristic, graph-struc-tured, sparse signal representation algorithm for overcomplete dictionaries that can be decomposed into subdictionaries and whose dictionary elements can be arranged in a hierarchy.(More)
– The fusion of multimodal sensor information often requires learning decision rules from samples of high-dimensional data. Each data dimension may only be weakly informative for the detection problem of interest. Also, it is not known a priori which components combine to form a lower-dimensional feature space that is most informative. To learn both the(More)
—Low-dimensional statistics of measurements play an important role in detection problems, including those encountered in sensor networks. In this work, we focus on learning low-dimensional linear statistics of high-dimensional measurement data along with decision rules defined in the low-dimensional space in the case when the probability density of the(More)