In International Conference on Artificial Intelligence and Statistics (AISTATS), April-May 2013.

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 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 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.