Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning statistics. In most real-world scenarios, however, problems are often ill-posed or the underlying model suffers from overfitting, leading erroneous trivial solutions. This dealt with by adding extra constraints, known regularization. this paper, we ...

  In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...