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