Computing the M most probable modes of a graphical model Conference Paper


Author(s): Chen, Chao; Kolmogorov, Vladimir; Yan, Zhu; Metaxas, Dimitris; Lampert, Christoph
Title: Computing the M most probable modes of a graphical model
Title Series: JMLR: W&CP
Affiliation IST Austria
Abstract: We introduce the M-modes problem for graphical models: predicting the M label configurations of highest probability that are at the same time local maxima of the probability landscape. M-modes have multiple possible applications: because they are intrinsically diverse, they provide a principled alternative to non-maximum suppression techniques for structured prediction, they can act as codebook vectors for quantizing the configuration space, or they can form component centers for mixture model approximation. We present two algorithms for solving the M-modes problem. The first algorithm solves the problem in polynomial time when the underlying graphical model is a simple chain. The second algorithm solves the problem for junction chains. In synthetic and real dataset, we demonstrate how M-modes can improve the performance of prediction. We also use the generated modes as a tool to understand the topography of the probability distribution of configurations, for example with relation to the training set size and amount of noise in the data.
Conference Title: AISTATS: Conference on Uncertainty in Artificial Intelligence
Volume: 31
Conference Dates: April 29- May 1, 2013
Conference Location: Scottsdale, AZ, USA
ISBN: 1938-7228
Publisher: JMLR  
Date Published: 2013-01-01
Start Page: 161
End Page: 169
URL:
Open access: no
IST Austria Authors
  1. Christoph Lampert
    87 Lampert
  2. Chao Chen
    14 Chen
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