Compressed sensing using ℓ 1 minimization has been widely and successfully applied. To
further enhance the sparsity, a non-convex and piecewise linear penalty is proposed. This …

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space
to model functions by neural networks as the curse of dimensionality (CoD) cannot be …

Deep neural networks (DNNs) have achieved great success in many data processing
applications. However, high computational complexity and storage cost make deep learning …

The kernel method is a tool that converts data to a kernel space where operation can be
performed. When converted to a high-dimensional feature space by using kernel functions …

Kernel methods are popular in nonlinear and nonparametric regression due to their solid
mathematical foundations and optimal statistical properties. However, scalability remains the …

Ridgeless regression has garnered attention among researchers, particularly in light of
the``Benign Overfitting''phenomenon, where models interpolating noisy samples …

Online learning is an efficient approach in machine learning and statistics, which iteratively
updates models upon the observation of a sequence of training examples. A representative …

In this paper, we establish a novel connection between the metric entropy growth and the
embeddability of function spaces into reproducing kernel Hilbert/Banach spaces. Metric …

The sufficient dimension reduction (SDR) with sparsity has received much attention for
analysing high-dimensional data. We study a nonparametric sparse kernel sufficient …

Ensemble learning methods, such as boosting, focus on producing a strong classifier based
on numerous weak classifiers. In this paper, we develop a novel ensemble learning method …