: While feature selection is very difficult for high dimensional, unstructured data such as face image, it may be much easier to do if the data can be faithfully transformed into l...
Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how...
Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
The privacy concerns associated with data analysis over social networks have spurred recent research on privacypreserving social network analysis, particularly on privacypreservin...
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-d...