Unsupervised domain adaptation has caught appealing attentions as it facilitates the
unlabeled target learning by borrowing existing well-established source domain knowledge …

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …

Benefiting from its effectiveness in subspace segmentation, low-rank representation (LRR)
and its variations have many applications in computer vision and pattern recognition, such …

This article focuses on the cross-corpus speech emotion recognition (SER) task. To
overcome the problem that the distribution of training (source) samples is inconsistent with …

The aim of this paper is to explore the use of backtrackless walks and prime cycles for
characterizing both labeled and unlabeled graphs. The reason for using backtrackless walks …

Supervised classification techniques are known to exploit physical information of the
analysed data, such as similarity, distribution and other low level features. Despite the …

Subspace learning for dimensionality reduction is an important topic in pattern analysis and
machine learning, and it has extensive applications in feature representation and image …

In this paper, we propose a novel graph-based semisupervised learning framework, called
joint sparse representation and embedding propagation learning (JSREPL). The idea of …

In this paper, a novel label propagation (LP) method is presented, called the manifold
adaptive label propagation (MALP) method, which is to extend original LP by integrating …

As the data acquirement technologies develop rapidly, both the amount and types of data
become larger and larger. However, noise and outliers usually attach to the data and then …