Cluster-weighted models (CWMs) extend finite mixtures of regressions (FMRs) in order to allow the distribution covariates contribute clustering process. In this article, we introduce 24 matrix-variate CWMs which are obtained by allowing both responses and each cluster be modelled one four existing skewed distributions or normal distribution. Endowed with greater flexibility, our able handle kin...

This paper presents a novel framework for denoising magnetic resonance images. The framework relies on adaptive Markov-random-field (MRF) image models that we infer nonparametrically from the corrupted input data itself. The proposed denoising method produces an optimal reconstruction based on principles in empirical-Bayesian estimation and information theory. Given the corrupted input data and...

A k-submodular function is an extension of a submodular function in that its input is given by k disjoint subsets instead of a single subset. For unconstrained nonnegative ksubmodular maximization, Ward and Živný proposed a constant-factor approximation algorithm, which was improved by the recent work of Iwata, Tanigawa and Yoshida presenting a 1/2-approximation algorithm. Iwata et al. also pro...

The multi-reference alignment (MRA) problem entails estimating an image from multiple noisy and rotated copies of itself. If the noise level is low, one can reconstruct by missing rotations, aligning images, averaging out noise. While accurate rotation estimation impossible if high, rotations still be approximated, thus provide indispensable information. In particular, learning approximation er...

We present a strictly lexical parsing model where all the parameters are based on the words. This model does not rely on part-of-speech tags or grammatical categories. It maximizes the conditional probability of the parse tree given the sentence. This is in contrast with most previous models that compute the joint probability of the parse tree and the sentence. Although the maximization of join...