Time Varying Undirected Graphs

Details Time Varying Undirected Graphs Time Varying Undirected Graphs

2008-02-20

arxiv.org/abs/0802.2758v4

The 21st Annual Conference on Learning Theory (COLT 2008), Helsinki, Finland

Statistics

Machine Learning

12 pages, 3 figures, to appear in COLT 2008

Scientific paper

Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using $\ell_1$ penalization methods. However, current methods assume that the data are independent and identically distributed. If the distribution, and hence the graph, evolves over time then the data are not longer identically distributed. In this paper, we show how to estimate the sequence of graphs for non-identically distributed data, where the distribution evolves over time.

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