We consider the problem of visual categorization with minimal supervision during training. We propose a partbased model that loosely captures structural information. We represent ...
Detecting unknown worms is a challenging task. Extant solutions, such as anti-virus tools, rely mainly on prior explicit knowledge of specific worm signatures. As a result, after t...
Robert Moskovitch, Nir Nissim, Dima Stopel, Clint ...
We develop a supervised dimensionality reduction method, called Lorentzian Discriminant Projection (LDP), for feature extraction and classification. Our method represents the str...
An idealized clustering algorithm seeks to learn a cluster-adjacency matrix such that, if two data points belong to the same cluster, the corresponding entry would be 1; otherwise ...