Sparse representation for machine learning has been exploited in past years. Several sparse representation based classification algorithms have been developed for some application...
Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization pro...
A new approach to regression regularization called the Pairwise Elastic Net is proposed. Like the Elastic Net, it simultaneously performs automatic variable selection and continuo...
Alexander Lorbert, David Eis, Victoria Kostina, Da...
Existing word similarity measures are not robust to data sparseness since they rely only on the point estimation of words' context profiles obtained from a limited amount of ...
Jun'ichi Kazama, Stijn De Saeger, Kow Kuroda, Masa...
In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks...
Wensi Xi, Edward A. Fox, Weiguo Fan, Benyu Zhang, ...