5,398 research outputs found
A backward procedure for change-point detection with applications to copy number variation detection
Change-point detection regains much attention recently for analyzing array or
sequencing data for copy number variation (CNV) detection. In such
applications, the true signals are typically very short and buried in the long
data sequence, which makes it challenging to identify the variations
efficiently and accurately. In this article, we propose a new change-point
detection method, a backward procedure, which is not only fast and simple
enough to exploit high-dimensional data but also performs very well for
detecting short signals. Although motivated by CNV detection, the backward
procedure is generally applicable to assorted change-point problems that arise
in a variety of scientific applications. It is illustrated by both simulated
and real CNV data that the backward detection has clear advantages over other
competing methods especially when the true signal is short
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
Indecomposable representations and oscillator realizations of the exceptional Lie algebra G_2
In this paper various representations of the exceptional Lie algebra G_2 are
investigated in a purely algebraic manner, and multi-boson/multi-fermion
realizations are obtained. Matrix elements of the master representation, which
is defined on the space of the universal enveloping algebra of G_2, are
explicitly determined. From this master representation, different
indecomposable representations defined on invariant subspaces or quotient
spaces with respect to these invariant subspaces are discussed. Especially, the
elementary representations of G_2 are investigated in detail, and the
corresponding six-boson realization is given. After obtaining explicit forms of
all twelve extremal vectors of the elementary representation with the highest
weight {\Lambda}, all representations with their respective highest weights
related to {\Lambda} are systematically discussed. For one of these
representations the corresponding five-boson realization is constructed.
Moreover, a new three-fermion realization from the fundamental representation
(0,1) of G_2 is constructed also.Comment: 29 pages, 4 figure
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