We propose the construction of an appearance manifold with embedded view-dependent covariance matrix to recognize 3D objects which are influenced by geometric distortions and quality degradation effects. The appearance manifold is used to capture the pose variability, while the covariance matrix is used to learn the distribution of samples for gaining noise-invariance. However, since the appear...

In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and similar kernel-based models such as SVM is, that the covariance function should be adapted to the modeled data. Frequently, the squared exponential covariance function is used as a default. However, this can lead to a...

The need to estimate variance-covariance matrix in a time series regression arises often in economic applications involving macroeconomic or finance data. In this paper, we study the behavior of two most popular covariance matrix estimators, namely the Kiefer, Vogelsang and Bunzel kernel estimator without truncation (Kiefer, Vogelsang and Bunzel 2000, KVB thereafter) and standard consistent ker...

We develop a method for estimating well-conditioned and sparse covariance and inverse covariance matrices from a sample of vectors drawn from a sub-Gaussian distribution in high dimensional setting. The proposed estimators are obtained by minimizing the quadratic loss function and joint penalty of `1 norm and variance of its eigenvalues. In contrast to some of the existing methods of covariance...

Variograms and covariance functions are key tools in geostatistics. However, various properties, characterizations, and decomposition theorems have been established for covariance functions only. We present analogous results for variograms and explore the connections to covariance functions. Our ndings include criteria for covariance functions on intervals, and we apply them to exponential mode...

We demonstrate the usefulness of a digital Gaussian filter to provide a distance-dependent reduction of background error covariance estimates generated from an ensemble of forecasts. These improved background error covariance estimates are used in a hybrid ensemble Kalman filter data assimilation scheme to generate a reduced-error ensemble of model initial conditions. The benefits of using the ...

Covariance functions are the equivalent of covariance matrices for traits with many, potentially infinitely many, records in which the covariances are defined as a function of age or time. They can be fitted for any source of variation, e.g. genetic, permanent environment or phenotypic. A suitable family of functions for covariance functions are orthogonal polynomials. These give the covariance...

Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter. Consistency and asymptotic normality are proved for the Maximum Likelihood and Cross Validation estimators of the covariance parameters. The a...

I examined patterns of covariation of three morphometric blossom characters [gland area (GA), gland-stigma distance (GSD), and bract length (BL)] within genets, among genets, and among populations of the tropical vine, Dalechampia scandens (Euphorbiaceae). Covariance between BL and GA was evenly distributed among the three levels. This observation, coupled with developmental information, indica...

Most HMM-based speech recognition systems use Gaussian mixtures as observation probability density functions. An important goal in all such systems is to improve parsimony. One method is to adjust the type of covariance matrices used. In this work, factored sparse inverse covariance matrices are introduced. Based on U DU factorization, the inverse covariance matrix can be represented using line...