1,844 research outputs found
Unipotent representations of real classical groups
Let be a complex orthogonal or complex symplectic group, and let
be a real form of , namely is a real orthogonal group, a
real symplectic group, a quaternionic orthogonal group, or a quaternionic
symplectic group. For a fixed parity , we
define a set of nilpotent
-orbits in (the Lie algebra of ). When
is the parity of the dimension of the standard module of , this is the set of the stably trivial special nilpotent orbits, which
includes all rigid special nilpotent orbits. For each , we construct all unipotent
representations of (or its metaplectic cover when is a real symplectic
group and is odd) attached to via the method of theta
lifting and show in particular that they are unitary
Gibbs Max-margin Topic Models with Data Augmentation
Max-margin learning is a powerful approach to building classifiers and
structured output predictors. Recent work on max-margin supervised topic models
has successfully integrated it with Bayesian topic models to discover
discriminative latent semantic structures and make accurate predictions for
unseen testing data. However, the resulting learning problems are usually hard
to solve because of the non-smoothness of the margin loss. Existing approaches
to building max-margin supervised topic models rely on an iterative procedure
to solve multiple latent SVM subproblems with additional mean-field assumptions
on the desired posterior distributions. This paper presents an alternative
approach by defining a new max-margin loss. Namely, we present Gibbs max-margin
supervised topic models, a latent variable Gibbs classifier to discover hidden
topic representations for various tasks, including classification, regression
and multi-task learning. Gibbs max-margin supervised topic models minimize an
expected margin loss, which is an upper bound of the existing margin loss
derived from an expected prediction rule. By introducing augmented variables
and integrating out the Dirichlet variables analytically by conjugacy, we
develop simple Gibbs sampling algorithms with no restricting assumptions and no
need to solve SVM subproblems. Furthermore, each step of the
"augment-and-collapse" Gibbs sampling algorithms has an analytical conditional
distribution, from which samples can be easily drawn. Experimental results
demonstrate significant improvements on time efficiency. The classification
performance is also significantly improved over competitors on binary,
multi-class and multi-label classification tasks.Comment: 35 page
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