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 contextdependent (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. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms Keywords Distance function, kernel trick

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Gang Wu, Edward Y. Chang, Navneet Panda
• Keywords: Data Mining, Distance Function, Effective Distance Function, KDD 2005, Keywords Distance Function