In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...
Performing data mining tasks in streaming data is considered a challenging research direction, due to the continuous data evolution. In this work, we focus on the problem of clust...
Maria Kontaki, Apostolos N. Papadopoulos, Yannis M...
Many real world applications such as sensor networks and other monitoring applications naturally generate probabilistic streams that are highly correlated in both time and space. ...
Hyperspectral imaging is a new technique which has become increasingly important in many remote sensing applications, including automatic target recognition for military and defen...
Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computation...