Distance Metric Learning VS. Fisher Discriminant Analysis

Babak Alipanahi, Michael Biggs, Ali Ghodsi

There has been much recent attention to the problem of learning an appropriate distance metric, using class labels or other side information. Some proposed algorithms are iterative and computationally expensive. In this paper, we show how to solve one of these methods with a closed-form solution, rather than using semidefinite programming. We provide a new problem setup in which the algorithm performs better or as well as some standard methods, but without the computational complexity. Furthermore, we show a strong relationship between these methods and the Fisher Discriminant Analysis.

Subjects: 12. Machine Learning and Discovery; Please choose a second document classification

Submitted: Apr 15, 2008


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