The problem of finding the shortest path from a path or graph has been quite widely
discussed. There are also many algorithms that are the solution to this problem. The …

This paper aims to propose a distributed semi-supervised learning (D-SSL) algorithm to
solve D-SSL problems, where training samples are often extremely large-scale and located …

The correntropy-induced loss (C-loss) has been employed in learning algorithms to improve
their robustness to non-Gaussian noise and outliers recently. Despite its success on robust …

We propose the Rescaled Pure Greedy Learning Algorithm (RPGLA) for solving the kernel-
based regression problem. The computational complexity of the RPGLA is less than the …

A novel image patch based example-based super-resolution algorithm is proposed for
benefitting from social image data. The proposed algorithm is designed based on matrix …

We investigate the regression problem in supervised learning by means of the weak
rescaled pure greedy algorithm (WRPGA). We construct learning estimator by applying the …

We propose the Weak Rescaled Pure Super Greedy Algorithm (WRPSGA) for approximation
with respect to a dictionary D in Hilbert space. The WRPSGA is simpler than some popular …

Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a
new atom from a specified dictionary via the steepest gradient descent (SGD) and then …

We first study the error performances of the Vector Weak Rescaled Pure Greedy Algorithm
for simultaneous approximation with respect to a dictionary D in a Hilbert space. We show …

It is known that to alleviate the performance deterioration caused by the outliers, the robust
support vector (SV) regression is proposed, which is essentially a convex optimization …