In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

The goal of few-shot learning is to learn the generalizability from seen to unseen data with
only a few samples. Most previous few-shot learning focus on learning generalizability …

Hybrid analog/digital precoding offers a compromise between hardware complexity and
system performance in millimeter wave (mmWave) systems. This type of precoding allows …

Adapting the classifier trained on a source domain to recognize instances from a new target
domain is an important problem that is receiving recent attention. In this paper, we present …

Stable local feature detection and representation is a fundamental component of many
image registration and object recognition algorithms. Mikolajczyk and Schmid (June 2003) …

The Grassmann manifold of linear subspaces is important for the mathematical modelling of
a multitude of applications, ranging from problems in machine learning, computer vision and …

DURING the last two decades, giant strides have been achieved in many aspects of
computational engineering and sciences. Higher-order mathematical models, better …

In this paper we propose a discriminant learning framework for problems in which data
consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we …

A general scheme for trust-region methods on Riemannian manifolds is proposed and
analyzed. Among the various approaches available to (approximately) solve the trust-region …

In this paper, we examine image and video-based recognition applications where the
underlying models have a special structure-the linear subspace structure. We discuss how …