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Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Data Representation Nonnegtive Matrix Factorization Graph Regularization Multiple Kernel Learning.
2012/9/18
Nonnegative Matrix Factorization (NMF) has been contin-uously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank ...
Critical Properties of $S^{4}$ System Restudied via Generalized Migdal-Kadanoff Bond-moving Renormalization
Critical Properties System Restudied via Generalized Bond-moving Renormalization
2012/9/17
We study the critical properties of the spin-continuousS4 system on the typical translational in-variant triangular lattices by combining the recently developed generalized Migdal-Kadanoff bond-moving...
Guaranteed Conservative Fixed Width Confidence Intervals Via Monte Carlo Sampling
Guaranteed Conservative Fixed Width Confidence Intervals Monte Carlo Sampling
2012/9/17
Monte Carlo methods are used to approximate the means,? of random variablesY, whose distributions are not known explicitly. The key idea is that the
average of a random sample,Y1,...,Yn, tends to 礱sn...
Consistent selection of tuning parameters via variable selection stability
kappa coefficient penalized regression selection consistency stability tuning
2012/9/17
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature,...
Minimax testing of a composite null hypothesis defined via a quadratic functional in the model of regression
Nonparametric hypotheses testing sharp asymptotics separation rates minimax approach high-dimensional regression.
2012/9/17
We consider the problem of testing a particular type of composite null hypothesis under a nonparametric multivariate regression model. For a given quadraticfunctional Q, the null hypothesis states tha...
Non-Convex Rank Minimization via an Empirical Bayesian Approach
Non-Convex Rank Minimization via Empirical Bayesian Approach
2012/9/19
In many applications that require matrix solutions of minimal rank, the underlying cost function is non-convex leading to an intractable, NP-hard optimization problem.Consequently, the convex nuclear ...
It is of increasing importance to develop learning methods for ranking. In contrast to many learning objectives, however, the ranking problem presents difficulties due to the fact that the space of pe...
Adaptation to anisotropy and inhomogeneity via dyadic piecewise polynomial selection
Adaptation to anisotropy inhomogeneity via dyadic piecewise polynomial selection
2011/3/23
This article is devoted to nonlinear approximation and estimation via piecewise polynomials built on partitions into dyadic rectangles. The approximation rate is studied over possibly inhomogeneous an...
Error Prediction and Model Selection via Unbalanced Expander Graphs
Error Prediction Model Selection Unbalanced Expander Graphs
2010/10/19
We investigate deterministic design matrices for the fundamental problems of error prediction and model selection. Our deterministic design matrices are constructed from unbalanced expander graphs, a...
Adaptive estimation of covariance matrices via Cholesky decomposition
Covariance matrix banding Cholesky decomposition
2010/10/19
This paper studies the estimation of a large covariance matrix. We introduce a novel procedure called ChoSelect based on the Cholesky factor of the inverse covariance. This method uses a dimension red...
Decomposition of neuronal assembly activity via empirical de-Poissonization
asymptotics compound Poisson process empirical characteristic function higher-order interactions jump measure
2009/9/16
Consider a compound Poisson process with jump measure $nu$ supported by finitely many positive integers. We propose a method for estimating $nu$ from a single, equidistantly sampled trajectory and dev...
Parameter estimation of ODE's via nonparametric estimators
Asymptotics M-estimator Nonparametric regression Parametric estimation Splines
2009/9/16
Ordinary differential equations (ODE's) are widespread models in physics, chemistry and biology. In particular, this mathematical formalism is used for describing the evolution of complex systems and ...
Honest variable selection in linear and logistic regression models via $ell_1$ and $ell_1 + ell_2$ penalization
penalty sparse consistent variable selection regression generalized linear models logistic regression
2009/9/16
This paper investigates correct variable selection in finite samples via $ell_1$ and $ell_1 + ell_2$ type penalization schemes. The asymptotic consistency of variable selection immediately follows fro...
Generalised linear mixed model analysis via sequential Monte Carlo sampling
generalised additive models longitudinal data analysis nonparametric regression sequential Monte Carlo sampler
2009/9/16
We present a sequential Monte Carlo algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performi...
Functional principal components analysis via penalized rank one approximation
Functional data analysis penalization regularization singular value decomposition
2009/9/16
Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in ...