### abstract ###
The method of  stable random projections  is a  tool for efficiently computing the  SYMBOL    distances using low memory, where  SYMBOL  is a tuning parameter
The method boils down to a statistical estimation task and various estimators have been proposed, based on the  geometric mean , the  harmonic mean , and the  fractional power  etc
This study proposes the optimal quantile  estimator, whose main operation is selecting , which is considerably less expensive than taking fractional power, the main operation in previous estimators
Our experiments report that the  optimal quantile  estimator is nearly one order of magnitude more computationally efficient than previous estimators
For  large-scale learning  tasks in which storing and computing pairwise distances is a serious bottleneck, this estimator should be desirable
In addition to its computational advantages, the  optimal quantile  estimator exhibits nice theoretical properties
It is more accurate than previous estimators when {SYMBOL }
We  derive its theoretical error bounds and  establish the explicit (i e , no hidden constants) sample complexity bound
### introduction ###
The method of  stable random projections  CITATION , as an efficient tool for computing pairwise distances in massive high-dimensional data,  provides a promising mechanism to tackle  some of the challenges in modern machine learning
In this paper, we provide an easy-to-implement algorithm for  stable random projections  which is both statistically accurate and computationally efficient
