Guest Editorial: Learning from multiple sources

Abstract

The increasing availability of numerous heterogeneous but related sources of data arises in many fields, such as bioinformatics, robotics, computer vision, information retrieval, and many others. Multiple sources of data can be viewed as different, related views of the same learning problem, where dependencies between the views could potentially take on complex structures. This gives rise to interesting and challenging machine learning problems where data sources are combined for learning. This framework encompasses several data fusion tasks and related topics, such as transfer learning, multitask learning, multiview learning, and learning under covariate shift. Often the general concept is to augment training data for each problem of interest with data from other partially related learning problems. Several approaches for inferring and exploiting complex relationships between sources have been presented. The advantages of the multiple source learning paradigm can be seen in situations where individual data sources are noisy, incomplete, and learning from more than one source can filter out problem-independent noise. Additionally, another aspect of multiple source learning is that data sources need only contain partially relevant information to the desired learning problem, making it possible to exploit a large number of data sources. This special issue on “Learning from Multiple Sources” was inspired by a successful (eponymous) NIPS workshop which took place in Whistler (Canada) on December 13, 2008. The workshop received twenty-three submissions, from which seven contributed talks and