We present a multi-label multiple kernel learning (MKL) formulation in which the data are embedded into a low-dimensional space directed by the instancelabel correlations encoded ...
— Emerging uncertain database applications often involve the cleansing (conditioning) of uncertain databases using additional information as new evidence for reducing the uncerta...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...
Model-based clustering of motion trajectories can be posed as the problem of learning an underlying mixture density function whose components correspond to motion classes with dif...
Background: Exhaustive methods of sequence alignment are accurate but slow, whereas heuristic approaches run quickly, but their complexity makes them more difficult to implement. ...