746 research outputs found
Feature selection guided by structural information
In generalized linear regression problems with an abundant number of
features, lasso-type regularization which imposes an -constraint on the
regression coefficients has become a widely established technique. Deficiencies
of the lasso in certain scenarios, notably strongly correlated design, were
unmasked when Zou and Hastie [J. Roy. Statist. Soc. Ser. B 67 (2005) 301--320]
introduced the elastic net. In this paper we propose to extend the elastic net
by admitting general nonnegative quadratic constraints as a second form of
regularization. The generalized ridge-type constraint will typically make use
of the known association structure of features, for example, by using temporal-
or spatial closeness. We study properties of the resulting "structured elastic
net" regression estimation procedure, including basic asymptotics and the issue
of model selection consistency. In this vein, we provide an analog to the
so-called "irrepresentable condition" which holds for the lasso. Moreover, we
outline algorithmic solutions for the structured elastic net within the
generalized linear model family. The rationale and the performance of our
approach is illustrated by means of simulated and real world data, with a focus
on signal regression.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS302 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
In this paper, we introduce a dictionary learning based approach applied to
the problem of real-time reconstruction of MR image sequences that are highly
undersampled in k-space. Unlike traditional dictionary learning, our method
integrates both global and patch-wise (local) sparsity information and
incorporates some priori information into the reconstruction process. Moreover,
we use a Dependent Hierarchical Beta-process as the prior for the group-based
dictionary learning, which adaptively infers the dictionary size and the
sparsity of each patch; and also ensures that similar patches are manifested in
terms of similar dictionary atoms. An efficient numerical algorithm based on
the alternating direction method of multipliers (ADMM) is also presented.
Through extensive experimental results we show that our proposed method
achieves superior reconstruction quality, compared to the other state-of-the-
art DL-based methods
Reflection methods for user-friendly submodular optimization
Recently, it has become evident that submodularity naturally captures widely
occurring concepts in machine learning, signal processing and computer vision.
Consequently, there is need for efficient optimization procedures for
submodular functions, especially for minimization problems. While general
submodular minimization is challenging, we propose a new method that exploits
existing decomposability of submodular functions. In contrast to previous
approaches, our method is neither approximate, nor impractical, nor does it
need any cumbersome parameter tuning. Moreover, it is easy to implement and
parallelize. A key component of our method is a formulation of the discrete
submodular minimization problem as a continuous best approximation problem that
is solved through a sequence of reflections, and its solution can be easily
thresholded to obtain an optimal discrete solution. This method solves both the
continuous and discrete formulations of the problem, and therefore has
applications in learning, inference, and reconstruction. In our experiments, we
illustrate the benefits of our method on two image segmentation tasks.Comment: Neural Information Processing Systems (NIPS), \'Etats-Unis (2013
Doctor of Philosophy
dissertationWith modern computational resources rapidly advancing towards exascale, large-scale simulations useful for understanding natural and man-made phenomena are becoming in- creasingly accessible. As a result, the size and complexity of data representing such phenom- ena are also increasing, making the role of data analysis to propel science even more integral. This dissertation presents research on addressing some of the contemporary challenges in the analysis of vector fields--an important type of scientific data useful for representing a multitude of physical phenomena, such as wind flow and ocean currents. In particular, new theories and computational frameworks to enable consistent feature extraction from vector fields are presented. One of the most fundamental challenges in the analysis of vector fields is that their features are defined with respect to reference frames. Unfortunately, there is no single ""correct"" reference frame for analysis, and an unsuitable frame may cause features of interest to remain undetected, thus creating serious physical consequences. This work develops new reference frames that enable extraction of localized features that other techniques and frames fail to detect. As a result, these reference frames objectify the notion of ""correctness"" of features for certain goals by revealing the phenomena of importance from the underlying data. An important consequence of using these local frames is that the analysis of unsteady (time-varying) vector fields can be reduced to the analysis of sequences of steady (time- independent) vector fields, which can be performed using simpler and scalable techniques that allow better data management by accessing the data on a per-time-step basis. Nevertheless, the state-of-the-art analysis of steady vector fields is not robust, as most techniques are numerical in nature. The residing numerical errors can violate consistency with the underlying theory by breaching important fundamental laws, which may lead to serious physical consequences. This dissertation considers consistency as the most fundamental characteristic of computational analysis that must always be preserved, and presents a new discrete theory that uses combinatorial representations and algorithms to provide consistency guarantees during vector field analysis along with the uncertainty visualization of unavoidable discretization errors. Together, the two main contributions of this dissertation address two important concerns regarding feature extraction from scientific data: correctness and precision. The work presented here also opens new avenues for further research by exploring more-general reference frames and more-sophisticated domain discretizations
Courbure discrète : théorie et applications
International audienceThe present volume contains the proceedings of the 2013 Meeting on discrete curvature, held at CIRM, Luminy, France. The aim of this meeting was to bring together researchers from various backgrounds, ranging from mathematics to computer science, with a focus on both theory and applications. With 27 invited talks and 8 posters, the conference attracted 70 researchers from all over the world. The challenge of finding a common ground on the topic of discrete curvature was met with success, and these proceedings are a testimony of this wor
Quantization of the Nonlinear Sigma Model Revisited
We revisit the subject of perturbatively quantizing the nonlinear sigma model
in two dimensions from a rigorous, mathematical point of view. Our main
contribution is to make precise the cohomological problem of eliminating
potential anomalies that may arise when trying to preserve symmetries under
quantization. The symmetries we consider are twofold: (i) diffeomorphism
covariance for a general target manifold; (ii) a transitive group of isometries
when the target manifold is a homogeneous space. We show that there are no
anomalies in case (i) and that (ii) is also anomaly-free under additional
assumptions on the target homogeneous space, in agreement with the work of
Friedan. We carry out some explicit computations for the -model. Finally,
we show how a suitable notion of the renormalization group establishes the
Ricci flow as the one loop renormalization group flow of the nonlinear sigma
model.Comment: 51 page
RG Domain Walls and Hybrid Triangulations
This paper studies the interplay between the N=2 gauge theories in three and
four dimensions that have a geometric description in terms of twisted
compactification of the six-dimensional (2,0) SCFT. Our main goal is to
construct the three-dimensional domain walls associated to any
three-dimensional cobordism. We find that we can build a variety of 3d theories
that represent the local degrees of freedom at a given domain wall in various
4d duality frames, including both UV S-dual frames and IR Seiberg-Witten
electric-magnetic dual frames. We pay special attention to Janus domain walls,
defined by four-dimensional Lagrangians with position-dependent couplings. If
the couplings on either side of the wall are weak in different UV duality
frames, Janus domain walls reduce to S-duality walls, i.e. domain walls that
encode the properties of UV dualities. If the couplings on one side are weak in
the IR and on the other weak in the UV, Janus domain walls reduce to RG walls,
i.e. domain walls that encode the properties of RG flows. We derive the 3d
geometries associated to both types of domain wall, and test their properties
in simple examples, both through basic field-theoretic considerations and via
comparison with quantum Teichmuller theory. Our main mathematical tool is a
parametrization and quantization of framed flat SL(K) connections on these
geometries based on ideal triangulations.Comment: 82+26 pages, 64 figure
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