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In this paper we consider the fundamental problem of semi-supervised kernel function learning. We propose a general regularized framework for learning a kernel matrix, and then demonstrate an equivalence between our proposed kernel matrix learning framework and a general linear transformation learning problem. Our result shows that the learned kernel matrices parameterize a linear transformation kernel function and can be applied inductively to new data points. Furthermore, our result gives a constructive method for kernelizing most existing Mahalanobis metric learning formulations. To make our results practical for large-scale data, we modify our framework to limit the number of parameters in the optimization process. We also consider the problem of kernelized inductive dimensionality reduction in the semi-supervised setting. We introduce a novel method for this problem by considering a special case of our general kernel learning framework where we select the trace norm function as the regularizer. We empirically demonstrate that our framework learns useful kernel functions, improving the $k$-NN classification accuracy significantly in a variety of domains. Furthermore, our kernelized dimensionality reduction technique significantly reduces the dimensionality of the feature space while achieving competitive classification accuracies.
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Video Length: 0
Date Found: March 26, 2011
Date Produced: March 25, 2011
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VideoLectures |
July 10, 2011
The explosion in growth of the Web of Linked Data has provided, for the first time, a plethora of information in disparate locations, yet bound together by machine-readable, semantically typed relations. Utilisation of the Web of Data has been, until now, restricted to members of the community, ...
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VideoLectures |
July 10, 2011
Problems cannot be solved with the mentality that has caused them’. Hence, the 2008- crisis cannot be solved with ethics of one-sided and short-term mentality of the industrial and neoliberal economics, which has caused the ‘Bubble Economy’ of several recent decades. Neither the market nor the ...
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VideoLectures |
July 10, 2011
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VideoLectures |
July 10, 2011
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VideoLectures |
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Social media presents unique challenges for topic classification, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a conversation. In this paper we investigate the usefulness of these external hyperlinks ...
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