We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recoverâ€¦ (More)

We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest areâ€¦ (More)

We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The timeâ€¦ (More)

Given sets of observations of training and test data, we consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. We achieve thisâ€¦ (More)

In many applications, relationships among objects of interest are more complex than pairwise. Simply approximating complex relationships as pairwise ones can lead to loss of information. Anâ€¦ (More)

We propose a technique for identifying latent Web communities based solely on the hyperlink structure of the WWW, via random walks. Although the topology of the Directed Web Graph encodes importantâ€¦ (More)

Discussions about different graph Laplaciansâ€”mainly the normalized and unnormalized versions of graph Laplacianâ€”have been ardent with respect to various methods of clustering and graph basedâ€¦ (More)

Real-world data often involves objects that exhibit multiple relationships; for example, â€˜papersâ€™ and â€˜authorsâ€™ exhibit both paperauthor interactions and paper-paper citation relationships. A typicalâ€¦ (More)

Dimensionality reduction is an essential aspect of visual processing. Traditionally, linear dimensionality reduction techniques such as principle components analysis have been used to find lowâ€¦ (More)