Abstract In this work, we propose an extension of the versatile joint regression framework for bivariate count responses package by Marra and Radice (R version 0.2-3, 2020) incorporating (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation method enables variable selection corresponding estimates guarantee shrinkage sparsity. Hence, approach p...

Abstract Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these focus primarily on predicting generally perceived preference an image, making them usually limited practicability, since each user may completely different preferences for same image. To address this problem, paper presents a nove...

where Y = (y1, · · · , yp) and ỹi = √ σyi,w̃i = wi/σ . These properties are used for the proof of the main results. Note: throughout the supplementary material, when evaluation is taken place at σ = σ̄, sometimes we omit the argument σ in the notation for simplicity. Also we use Y = (y1, · · · , yp) to denote a generic sample and use Y to denote the p× n data matrix consisting of n i.i.d. such sa...

For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than matching the common structures with small deformations in the two images. Most existing works depend heavily on the outlier segmentation to remove the outlier effect in the registration. Moreover, these works do not ha...

The focus of this study is on statistical analysis of categorical responses, where the response values are dependent of each other. The most typical example of this kind of dependence is when repeated responses have been obtained from the same study unit. For example, in Paper I, the response of interest is the pneumococcal nasopharengyal carriage (yes/no) on 329 children. For each child, the c...

Multivariate regression model is a natural generalization of the classical univariate regression model for fitting multiple responses. In this paper, we propose a highdimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accounts for the dependency structure among the multiple responses. The proposed method...

The problem of feature selection has aroused considerable research interests in the past few years. Traditional learning based feature selection methods separate embedding learning and feature ranking. In this paper, we introduce a novel unsupervised feature selection approach via Joint Embedding Learning and Sparse Regression (JELSR). Instead of simply employing the graph laplacian for embeddi...

Single image super-resolution (SR) methods can be broadly categorized into three classes: interpolation-based methods, reconstruction-based methods [7], and example-based methods [2, 3, 6]. The reconstruction-based methods often incorporate prior knowledge to regularize the ill-posed problem. For example, Zhang et al. [7] assembled the Steering Kernel Regression [5] (SKR)-based local prior and ...

A graphical model is used for describing interrelationships among multiple variables. In many cases, the multivariate Gaussian assumption is made partly for its simplicity but the assumption is hardly met in actual applications. In order to avoid dependence on a rather strong assumption, we propose to infer the graphical model via joint quantile regression with component selection, since the co...