In this paper, we propose an efficient algorithm, called vector field consensus, for
establishing robust point correspondences between two sets of points. Our algorithm starts …

Often, tasks are collected for multi-task learning (MTL) because they share similar feature
structures. Based on this observation, in this paper, we present novel algorithm-dependent …

No single feature can satisfactorily characterize the semantic concepts of an image.
Multiview learning aims to unify different kinds of features to produce a consensual and …

In many data analysis tasks, one is often confronted with very high dimensional data. The
manifold assumption, which states that the data is sampled from a submanifold embedded in …

Most existing works on multi-task learning (MTL) assume the same input space for different
tasks. In this paper, we address a general setting where different tasks have heterogeneous …

Recently, orthogonal recurrent neural networks (RNNs) have emerged as state-of-the-art
models for learning long-term dependencies. This class of models mitigates the exploding …

Projective non-negative matrix factorization (PNMF) learns a subspace spanned by several
non-negative bases by minimizing the distance between samples and their reconstructions …

In many fields one needs to build predictive models for a set of related machine learning
tasks, such as information retrieval, computer vision and biomedical informatics …

Aiming at the fact of lower efficiency and higher time cost for feature matching in aircraft
surface texture mapping process, a novel mismatch removal method based on Gaussian …

Statistical learning theory provides the mathematical and theoretical foundations for
statistical learning algorithms and inspires the development of more efficient methods. It is …