The Kernel Semi–Least Squares Method for Sparse Distance Approximation

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.