We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common i...
We present a framework for computing optimal transformations, aligning one point set to another, in the presence of outliers. Example applications include shape matching and regis...
We present a method for learning discriminative linear feature extraction using independent tasks. More concretely, given a target classification task, we consider a complementary...
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
In many vision problems, instead of having fully labeled training data, it is easier to obtain the input in small groups, where the data in each group is constrained to be from th...