  In this short letter we present the construction of a bi-stochastic kernel p
for an arbitrary data set X that is derived from an asymmetric affinity
function {\alpha}. The affinity function {\alpha} measures the similarity
between points in X and some reference set Y. Unlike other methods that
construct bi-stochastic kernels via some convergent iteration process or
through solving an optimization problem, the construction presented here is
quite simple. Furthermore, it can be viewed through the lens of out of sample
extensions, making it useful for massive data sets.
