Elastic-Net Regularization in Learning Theory

Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie for the selection of groups of correlated variables. To investigate on the statistical properties of this scheme and in particular on its consistency properties, we set up a suitable mathematical framework. Our setting is random-design regression where we allow the response variable to be vector-valued and we consider prediction functions which are linear combination of elements ({\em features}) in an infinite-dimensional dictionary. Under the assumption that the regression function admits a sparse representation on the dictionary, we prove that there exists a particular ``{\em elastic-net representation}'' of the regression function such that, if the number of data increases, the elastic-net estimator is consistent not only for prediction but also for variable/feature selection. Our results include finite-sample bounds and an adaptive scheme to select the regularization parameter. Moreover, using convex analysis tools, we derive an iterative thresholding algorithm for computing the elastic-net solution which is different from the optimization procedure originally proposed by Zou and HastieComment: 32 pages, 3 figure

An evolutionary model for the gamma-ray system PSR J1311-3430 and its companion

The most recent member of the millisecond pulsar with very low-mass companions and short orbital periods class, PSR J1311-3430 (Pletsch et al. 2012) is a remarkable object in various senses. Besides being the first discovered in gamma-rays, its measured features include the very low or absent hydrogen content. We show in this Letter that this important piece of information leads to a very restricted range of initial periods for a given donor mass. For that purpose, we calculate in detail the evolution of the binary system self-consistently, including mass transfer and evaporation, finding the features of the new evolutionary path leading to the observed configuration. It is also important to remark that the detailed evolutionary history of the system naturally leads to a high final pulsar mass, as it seems to be demanded by observations.Comment: 5 pages, 5 figures, 1 table. Accepted for publication in MNRAS Letter

Positive operator valued measures covariant with respect to an irreducible representation

Given an irreducible representation of a group G, we show that all the covariant positive operator valued measures based on G/Z, where Z is a central subgroup, are described by trace class, trace one positive operators.Comment: 9 pages, Latex2

Adaptive Kernel Methods Using the Balancing Principle

The regularization parameter choice is a fundamental problem in supervised learning since the performance of most algorithms crucially depends on the choice of one or more of such parameters. In particular a main theoretical issue regards the amount of prior knowledge on the problem needed to suitably choose the regularization parameter and obtain learning rates. In this paper we present a strategy, the balancing principle, to choose the regularization parameter without knowledge of the regularity of the target function. Such a choice adaptively achieves the best error rate. Our main result applies to regularization algorithms in reproducing kernel Hilbert space with the square loss, though we also study how a similar principle can be used in other situations. As a straightforward corollary we can immediately derive adaptive parameter choice for various kernel methods recently studied. Numerical experiments with the proposed parameter choice rules are also presented

Local Spatio-Temporal Representation Using the 3D Shearlet Transform (STSIP)

In this work we address the problem of analyzing video sequences and of representing meaningful space-time points of interest by using the 3D shearlet transform. We introduce a local representation based on shearlet coe cients of the video, regarded as 2D+T signal. This representation turns out to be informative to understand the local spatio-temporal characteristics, which can be easily detected by an unsupervised clustering algorithm

Group Theoretical Quantum Tomography

The paper is devoted to the mathematical foundation of the quantum tomography using the theory of square-integrable representations of unimodular Lie groups.Comment: 13 pages, no figure, Latex2e. Submitted to J.Math.Phy