The kernel semi-least squares method for sparse distance approximation
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Samuel Epstein1, Margrit Betke
1Department of Computer Science, Boston University, Boston, MA 02215, USA. samepst@cs.bu.edu
Neural Computation
|November 15, 2012
Abstract:
We extend the semi-least squares problem defined by Rao and Mitra ( 1971 ) to the kernel semi-least squares problem. We introduce subset projection, a technique that produces a solution to this problem. We show how the results of subset projection can be used to approximate a computationally expensive distance metric.
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