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The method of stable random projections is an efficient tool for computing the l<sub>alpha</sub> distances using low memory, where 0 &lt; alpha les 2 may be viewed as a tuning parameter. This method boils down to a statistical estimation task and various estimators have been proposed, based on the geometric mean, harmonic mean, and 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 this estimator is nearly one order of magnitude more computationally efficient than previous estimators. For large-scale tasks in which storing and computing pairwise distances is a serious bottleneck, this estimator should be desirable. In addition to its computational advantage, the optimal quantile estimator exhibits nice theoretical properties. It is more accurate than previous estimators when alpha &gt; 1. We derive its theoretical error bound and establish the explicit (i.e., no hidden constants) sample complexity bound.

Computationally Efficient Estimators for Dimension Reductions Using Stable Random Projections