Random projection has been suggested as a means of dimensionality reduction, where the original data are projected onto a subspace using a random matrix. It represents a computati...
Tetsuya Takiguchi, Jeff Bilmes, Mariko Yoshii, Yas...
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however,...
In recent years, random projection has been used as a valuable tool for performing dimensionality reduction of high dimensional data. Starting with the seminal work of Johnson and...
In this paper, we develop a stochastic approximation method to solve a monotone estimation problem and use this method to enhance the empirical performance of the Q-learning algor...
The Wiener index is analyzed for random recursive trees and random binary search trees in the uniform probabilistic models. We obtain the expectations, asymptotics for the varianc...