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While the machine learning community has primarily focused on analysing the output of a single data source, there has been relatively few attempts to develop a general framework, or heuristics, for analysing several data sources in terms of a shared dependency structure. Learning from multiple data sources (or alternatively, the data fusion problem) is a timely research area. Due to the increasing availability and sophistication of data recording techniques and advances in data analysis algorithms, there exists many scenarios in which it is necessary to model multiple, related data sources, i.e. in fields such as bioinformatics, multimodal signal processing, information retrieval, sensor networks etc. The open question is to find approaches to analyse data which consists of more than one set of observations (or view) of the same phenomenon. In general, existing methods use a discriminative approach, where a set of features for each data set is found in order to explicitly optimise some dependency criterion. However, a discriminative approach may result in an ad hoc algorithm, require regularisation to ensure erroneous shared features are not discovered, and it is difficult to incorporate prior knowledge about the shared information. A possible solution is to overcome these problems is a generative probabilistic approach, which models each data stream as a sum of a shared component and a private component that models the withinset variation. In practice, related data sources may exhibit complex covariation (for instance, audio and visual streams related to the same video) and therefore it is necessary to develop models that impose structured variation within and between data sources, rather than assuming a socalled 'flat' data structure. Additional methodological challenges include determining what is the 'useful' information to extract from the multiple data sources, and building models for predicting one data source given the others. Finally, as well as learning from multiple data sources in an unsupervised manner, there is the closely related problem of multitask learning, or transfer learning where a task is learned from other related tasks.
Author Information
David R Hardoon (SAS)
Gayle Leen (Helsinki University of Technology)
Samuel Kaski (Aalto University and University of Helsinki)
John ShaweTaylor (UCL)
John ShaweTaylor has contributed to fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, driving the mapping of these approaches onto novel domains including work in computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have together attracted over 60000 citations. He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.
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