Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction
Methodology
2015-07-27 v2 Machine Learning
Abstract
We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package "LICORS" (http://cran.r-project.org/web/packages/LICORS/).
Cite
@article{arxiv.1211.3760,
title = {Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction},
author = {Georg M. Goerg and Cosma Rohilla Shalizi},
journal= {arXiv preprint arXiv:1211.3760},
year = {2015}
}
Comments
11 pages; AISTATS 2013