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Consider the problem of learning logistic-regression models for multiple classification tasks, where the training data set for each task is not drawn from the same statistical distribution. In such a multi-task learning (MTL) scenario, it is necessary to identify groups of similar tasks that should be learned jointly. Relying on a Dirichlet process (DP)(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 logistic regression classification algorithm is developed for problems in which the feature vectors may be missing data (features). Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function (conditioned on the non-missing data). Conditional density functions are(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)
In the search for diagnostic and therapeutic strategies for lung cancer, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been evinced as a new and promising discovery platform to generate protein expression profiles in search of overexpressed proteins in lung tumors. Data from MALDI-TOF spectra require(More)
We address the incomplete-data problem in which feature vectors to be classified are missing data (features). A (supervised) logistic regression algorithm for the classification of incomplete data is developed. Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function(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 introduce <i>quadratically gated mixture of experts</i> (QGME), a statistical model for multi-class nonlinear classification. The QGME is formulated in the setting of incomplete data, where the data values are partially observed. We show that the missing values entail joint estimation of the data manifold and the classifier, which allows <i>adaptive</i>(More)