Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in...
There has been a recent surge in work in probabilistic databases, propelled in large part by the huge increase in noisy data sources -from sensor data, experimental data, data fro...
Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These tech...
Current Web search engines generally impose link analysis-based re-ranking on web-page retrieval. However, the same techniques, when applied directly to small web search such as i...