Given a finite number of data points sampled from a low-dimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data poin...
Recently the problem of dimensionality reduction (or, subspace learning) has received a lot of interests in many fields of information processing, including data mining, informati...
The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-super...
Learning the common structure shared by a set of supervised tasks is an important practical and theoretical problem. Knowledge of this structure may lead to better generalization ...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied ...