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dc.contributor.advisorDaume III, Halen_US
dc.contributor.authorKumar, Abhisheken_US
dc.date.accessioned2013-10-09T05:34:31Z
dc.date.available2013-10-09T05:34:31Z
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1903/14600
dc.description.abstractThe notion of similarities between data points is central to many classification and clustering algorithms. We often encounter situations when there are more than one set of pairwise similarity graphs between objects, either arising from different measures of similarity between objects or from a single similarity measure defined on multiple data representations, or a combination of these. Such examples can be found in various applications in computer vision, natural language processing and computational biology. Combining information from these multiple sources is often beneficial in learning meaningful concepts from data. This dissertation proposes novel methods to effectively fuse information from these multiple similarity graphs, targeted towards two fundamental tasks in machine learning - classification and clustering. In particular, I propose two models for learning spectral embedding from multiple similarity graphs using ideas from co-training and co-regularization. Further, I propose a novel approach to the problem of multiple kernel learning (MKL), converting it to a more familiar problem of binary classification in a transformed space. The proposed MKL approach learns a ``good'' linear combination of base kernels by optimizing a quality criterion that is justified both empirically and theoretically. The ideas of the proposed MKL method are also extended to learning nonlinear combinations of kernels, in particular, polynomial kernel combination and more general nonlinear kernel combination using random forests.en_US
dc.titleLearning with Multiple Similaritiesen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledco-regularizationen_US
dc.subject.pquncontrolledco-trainingen_US
dc.subject.pquncontrolledmachine learningen_US
dc.subject.pquncontrolledmultiple kernel learningen_US
dc.subject.pquncontrolledmultiviewen_US
dc.subject.pquncontrolledspectral clusteringen_US


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