To utilize both labeled data and unlabeled data in real-world applications, semi-supervised
learning is widely used as an effective technique. However, most semi-supervised methods …

Semi-supervised multi-view learning has been an important research topic due to its
capability to exploit complementary information from unlabeled multi-view data. This work …

Graph-based semi-supervised learning, which further utilizes graph structure behind
samples for boosting semi-supervised learning, gains convincing results in several machine …

Semi-supervised learning is an effective method to study how to use both labeled data and
unlabeled data to improve the performance of the classifier, which has become the hot field …

Graph-based semi-supervised learning has recently proved to be a powerful paradigm for
processing and mining large datasets. The main advantage relies on the fact that these …

In this article, we present a semantic semisupervised learning (Semantic SSL) approach
targeted at unifying two machine-learning paradigms in a mutually beneficial way, where the …

Positive and Unlabeled (PU) learning has attracted intensive research interests in recent
years, which is capable of training a binary classifier solely based on positive and unlabeled …

In doubly stochastic (DS) clustering, it is common to initialize the DS matrix with a similarity
matrix and use the eigen-decomposition of the DS-scaled similarity matrix to obtain the …

We present a probabilistic semi-supervised learning (SSL) framework based on sparse
graph structure learning. Different from existing SSL methods with either a predefined …

As one of the learning ways for inducing efficient image compact representation, graph
embedding (GE) based manifold learning has been widely developed over the last two …