document: Formulating distance functions via the kernel trick Tasks of data mining and information retrieval depend on a good distance function for measuring similarity between data instances . The most effective distance function must be formulated in a context-dependent ( also application - , data - , and user-dependent ) way . In this paper , we propose to learn a distance function by capturing the nonlinear relationships among contextual information provided by the application , data , or user . We show that through a process called the `` kernel trick , '' such nonlinear relationships can be learned efficiently in a projected space . Theoretically , we substantiate that our method is both sound and optimal . Empirically , using several datasets and applications , we demonstrate that our method is effective and useful .
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