Markov network estimation from multi-attribute data

Mladen Kolar, Han Liu, Eric P. Xing

Research output: Contribution to conferencePaper

6 Scopus citations

Abstract

Many real world network problems often concern multivariate nodal attributes such as image, textual, and multi-view feature vectors on nodes, rather than simple univariate nodal attributes. The existing graph estimation methods built on Gaussian graphical models and covariance selection algorithms can not handle such data, neither can the theories developed around such methods be directly applied. In this paper, we propose a new principled framework for estimating multi-attribute graphs. Instead of estimating the partial correlation as in current literature, our method estimates the partial canonical correlations that naturally accommodate complex nodal features. Computationally, we provide an efficient algorithm which utilizes the multi-attribute structure. Theoretically, we provide sufficient conditions which guarantee consistent graph recovery. Extensive simulation studies demonstrate performance of our method under various conditions.

Original languageEnglish (US)
Pages1110-1118
Number of pages9
StatePublished - Jan 1 2013
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: Jun 16 2013Jun 21 2013

Conference

Conference30th International Conference on Machine Learning, ICML 2013
CountryUnited States
CityAtlanta, GA
Period6/16/136/21/13

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Sociology and Political Science

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  • Cite this

    Kolar, M., Liu, H., & Xing, E. P. (2013). Markov network estimation from multi-attribute data. 1110-1118. Paper presented at 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, United States.