Learning Linear Dynamical Systems without Sequence Information (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.

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Approximate BibTeX Entry

@proceedings{tk_jeff_icml2009,
    Year = {2009},
    Booktitle = {ICML 2009: Proceedings of the 26th International Conference on Machine Learning},
    Author = { Tzu-Kuo Huang, Jeff Schneider },
    Title = {Learning Linear Dynamical Systems without Sequence Information}
}