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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

February 2013, Vol. 25, No. 2, Pages 532-548
(doi: 10.1162/NECO_a_00401)
© 2013 Massachusetts Institute of Technology
The Kernel Semi–Least Squares Method for Sparse Distance Approximation
Article PDF (587.32 KB)
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.