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 Mmodes problem for graphical models: predicting the M label configurations of highest probability that are at the same time local maxima of the probability landscape. Mmodes have multiple possible applications: because they are intrinsically diverse, they provide a principled alternative to nonmaximum 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 Mmodes 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 Mmodes 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:

19387228

Publisher:

JMLR

Date Published:

20130101

Start Page: 
161

End Page:

169

URL: 

Open access: 
no 