Abstract Flexible Manifold Embedding (FME) has been recently proposed as a semi-
supervised graph-based label propagation method. It aims at estimating simultaneously the …

In semi-supervised label propagation (LP), the data manifold is approximated by a graph,
which is considered as a similarity metric. Graph estimation is a crucial task, as it affects the …

Label propagation is one of the state-of-the-art methods for semi-supervised learning, which
estimates labels by propagating label information through a graph. Label propagation …

This paper introduces a graph-based semi-supervised embedding method as well as its
kernelized version for generic classification and recognition tasks. The aim is to combine the …

Graph-based learning in semisupervised models provides an effective tool for modeling big
data sets in high-dimensional spaces. It has been useful for propagating a small set of initial …

This paper introduces two graph-based semi-supervised embedding methods for generic
classification and recognition tasks. These proposed methods combine the merits of Flexible …

In this paper, we address the problem of large-scale graph-based semi-supervised learning
for multi-class classification. Most existing scalable graph-based semi-supervised learning …

Classified labels are expensive by virtue of the utilization of field knowledge while the
unlabeled data contains significant information, which can not be explored by supervised …

Recently, graph-based semi-supervised learning (GSSL) has received much attention. On
the other hand, less attention has been paid to the problem of large-scale GSSL for inductive …

Kernel methods have been successfully applied to the areas of pattern recognition and data
mining. In this paper, we mainly discuss the issue of propagating labels in kernel space. A …