Learning Linear Dynamical Systems without Sequence Information - Robotics Institute Carnegie Mellon University

Learning Linear Dynamical Systems without Sequence Information

T. Huang and J. Schneider
Conference Paper, Proceedings of (ICML) International Conference on Machine Learning, pp. 425 - 432, June, 2009

Abstract

Virtually all methods of learning dynamic systems from data start from the same
basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of data generated from the dynamic system. In this paper we consider the case where the data is not sequenced. The learning algorithm is presented a set of data points from the system's operation but with no temporal ordering. The data are simply drawn as individual disconnected points. While making this assumption may seem absurd at first glance, we observe that many scientific modeling tasks have exactly this property. In this paper we restrict our attention to learning linear, discrete time models. We propose several algorithms for learning these models based on optimizing approximate likelihood functions and test the methods on several synthetic data sets.

BibTeX

@conference{Huang-2009-119818,
author = {T. Huang and J. Schneider},
title = {Learning Linear Dynamical Systems without Sequence Information},
booktitle = {Proceedings of (ICML) International Conference on Machine Learning},
year = {2009},
month = {June},
pages = {425 - 432},
}