|
Activity Number:
|
253
- Contributed Poster Presentations: Section on Statistical Computing
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, July 30, 2018 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Statistical Computing
|
|
Abstract #329643
|
|
|
Title:
|
Statistical Techniques to Improve Random Projections and Other Similar Algorithms
|
|
Author(s):
|
Keegan Kang* and Weipin Wong and Haikal Yeo
|
|
Companies:
|
Singapore University Of Technology And Design and Singapore University Of Technology and Design and Independent
|
|
Keywords:
|
dimension reduction;
random projections;
bayesian prior;
control variates;
maximum likelihood estimates
|
|
Abstract:
|
Random projections are used to approximately preserve distances in high dimensional space. A transformation involving random variables is applied to a dataset, mapping it to a smaller subspace. The original distance is then estimated via an unbiased estimator. We show how we can apply statistical techniques such as maximum likelihood estimates, control variates, and Bayesian priors to improve the accuracy of such estimates with negligible computational cost. We also show how our techniques can be generalized to similar algorithms as well.
|
Authors who are presenting talks have a * after their name.
