Learn More
Clustering large data is a fundamental problem with a vast number of applications. Due to the increasing size of data, practitioners interested in clustering have turned to distributed computation methods. In this work, we consider the widely used k-center clustering problem and its variant used to handle noisy data, k-center with outliers. In the(More)
We introduce a novel information-theoretic approach for active model selection and demonstrate its effectiveness in a real-world application. Although our method can work with arbitrary models, we focus on actively learning the appropriate structure for Gaussian process (GP) models with arbitrary observation likelihoods. We then apply this framework to(More)
Image representation is an essential issue regarding the problems related to image processing and understanding. In the last years, the sparse representation modelling for signals has been receiving a lot of attention due to its state-of-the art performance in different computer vision tasks. One of the important factors to its success is the ability to(More)
Despite the success of kernel-based nonparametric methods, kernel selection still requires considerable expertise, and is often described as a " black art. " We present a sophisticated method for automatically searching for an appropriate kernel from an infinite space of potential choices. Previous efforts in this direction have focused on traversing a(More)
In recent years, the sparse representation modeling of signals has received a lot of attention due to its state-of-the-art performance in different computer vision tasks. One important factor to its success is the ability to promote representations that are well adapted to the data. This is achieved by the use of dictionary learning algorithms. The most(More)
  • 1