A novel neural network, namely, broad learning system (BLS), has shown impressive
performance on various regression and classification tasks. Nevertheless, most BLS models …

Triplet learning, ie learning from triplet data, has attracted much attention in computer vision
tasks with an extremely large number of categories, eg, face recognition and person re …

Most existing feature selection methods in literature are linear models, so that the nonlinear
relations between features and response variables are not considered. Meanwhile, in these …

Additive models have attracted much attention for high-dimensional regression estimation
and variable selection. However, the existing models are usually limited to the single-task …

Additive models have been burgeoning in data analysis due to their flexible representation
and desirable interpretability. However, most existing approaches are constructed under …

In this paper, we consider high-dimensional regression with a ℓ 0 constraint. Such
optimization problems were once thought to be hard to solve, but recent advances in …

In the recent past, logarithmic hyperbolic cosine based-cost function has been widely
applied in adaptive filters as it offers robust performance against outliers. However, the …

Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been
formulated by employing the hybrid error metric of “pointwise loss+ pairwise loss” and have …

This article devises a spline adaptive filter (SAF) algorithm with robustness for nonlinear
adaptive noise cancellation (NANC), called the SAF-SDM, which is built on a distance …

Partially linear models (PLMs), rooted in the combination of linear and nonlinear
approximation, are recognized to be capable of modeling complex data. Indeed, the …