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Multi-task learning (MTL) is considered for logistic-regression classifiers, based on a Dirichlet process (DP) formulation. A symmetric MTL (SMTL) formulation is considered in which classifiers for multiple tasks are learned jointly, with a variational Bayesian (VB) solution. We also consider an asymmetric MTL (AMTL) formulation in which the posterior(More)
A mobile electromagnetic-induction (EM I) sensor is considered for detection and characterization of buried conducting and/or ferrous targets. The sensor maybe placed on a robot and, here, we consider design of an optimal adaptive-search strategy. A frequency-dependent magnetic-dipole model is used to characterize the target at EMI frequencies. The goal of(More)
An approach to identifying ground targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multi-aspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical(More)
We consider the group basis pursuit problem, which extends basis pursuit by replacing the l1 norm with a weighted-l2,1 norm. We provide an anytime algorithm, called generalized alternating projection (GAP), to solve this problem. The GAP algorithm extends classical alternating projection to the case in which projections are performed between convex sets(More)
Context plays an important role when performing classification, and in this paper we examine context from two perspectives. First, the classification of items within a single task is placed within the context of distinct concurrent or previous classification tasks (multiple distinct data collections). This is referred to as multi-task learning (MTL), and is(More)
A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel(More)