Encoding large information structures in linear algebra and statistical models
Machine Learning
2022-06-23 v3
Abstract
Large information sizes in samples and features can be encoded to speed up the learning of statistical models based on linear algebra and remove unwanted signals. Encoding information can reduce both sample and feature dimension to a smaller representational set. Here two examples are shown on linear mixed models and mixture models speeding up the run time for parameter estimation by a factor defined by the user's choice on dimension reduction (can be linear, quadratic or beyond based on dimension specification).
Cite
@article{arxiv.2201.08233,
title = {Encoding large information structures in linear algebra and statistical models},
author = {David Banh and Alan Huang},
journal= {arXiv preprint arXiv:2201.08233},
year = {2022}
}