A Geometric Framework for Transfer Learning Using Manifold Alignment
Many machine learning problems involve dealing with a large amount of high-dimensional data across diverse domains. In addition, annotating or labeling the data is expensive as it involves significant human effort. This dissertation explores a joint solution to both these problems by exploiting the...
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ScholarWorks@UMass Amherst
2010
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| Online Access: | https://scholarworks.umass.edu/open_access_dissertations/269 https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1269&context=open_access_dissertations |