On Practical Algorithms for Entropy Estimation and the Improved Sample Complexity of Compressed Counting
Data Structures and Algorithms
2015-03-14 v1 Methodology
Abstract
Estimating the p-th frequency moment of data stream is a very heavily studied problem. The problem is actually trivial when p = 1, assuming the strict Turnstile model. The sample complexity of our proposed algorithm is essentially O(1) near p=1. This is a very large improvement over the previously believed O(1/eps^2) bound. The proposed algorithm makes the long-standing problem of entropy estimation an easy task, as verified by the experiments included in the appendix.
Cite
@article{arxiv.1004.3782,
title = {On Practical Algorithms for Entropy Estimation and the Improved Sample Complexity of Compressed Counting},
author = {Ping Li},
journal= {arXiv preprint arXiv:1004.3782},
year = {2015}
}