Search results for "Kernel"
showing 10 items of 357 documents
Estimating biophysical variable dependences with kernels
2010
This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationshi…
On properties of the iterative maximum likelihood reconstruction method
1989
In this paper, we continue our investigations6 on the iterative maximum likelihood reconstruction method applied to a special class of integral equations of the first kind, where one of the essential assumptions is the positivity of the kernel and the given right-hand side. Equations of this type often occur in connection with the determination of density functions from measured data. There are certain relations between the directed Kullback–Leibler divergence and the iterative maximum likelihood reconstruction method some of which were already observed by other authors. Using these relations, further properties of the iterative scheme are shown and, in particular, a new short and elementar…
Kernelizing LSPE(λ)
2007
We propose the use of kernel-based methods as underlying function approximator in the least-squares based policy evaluation framework of LSPE(λ) and LSTD(λ). In particular we present the 'kernelization' of model-free LSPE(λ). The 'kernelization' is computationally made possible by using the subset of regressors approximation, which approximates the kernel using a vastly reduced number of basis functions. The core of our proposed solution is an efficient recursive implementation with automatic supervised selection of the relevant basis functions. The LSPE method is well-suited for optimistic policy iteration and can thus be used in the context of online reinforcement learning. We use the hig…
An Introduction to Kernel Methods
2009
Machine learning has experienced a great advance in the eighties and nineties due to the active research in artificial neural networks and adaptive systems. These tools have demonstrated good results in many real applications, since neither a priori knowledge about the distribution of the available data nor the relationships among the independent variables should be necessarily assumed. Overfitting due to reduced training data sets is controlled by means of a regularized functional which minimizes the complexity of the machine. Working with high dimensional input spaces is no longer a problem thanks to the use of kernel methods. Such methods also provide us with new ways to interpret the cl…
Explicit recursivity into reproducing kernel Hilbert spaces
2011
This paper presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces (RKHS). Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define model recursivity in the Hilbert space. The method exploits some properties of functional analysis and recursive computation of dot products without the need of pre-imaging. We illustrate the feasibility of the methodology in the particular case of the gamma-filter, an infinite impulse response (IIR) filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and electroencephalographic time se…
Gradient estimates for heat kernels and harmonic functions
2020
Let $(X,d,\mu)$ be a doubling metric measure space endowed with a Dirichlet form $\E$ deriving from a "carr\'e du champ". Assume that $(X,d,\mu,\E)$ supports a scale-invariant $L^2$-Poincar\'e inequality. In this article, we study the following properties of harmonic functions, heat kernels and Riesz transforms for $p\in (2,\infty]$: (i) $(G_p)$: $L^p$-estimate for the gradient of the associated heat semigroup; (ii) $(RH_p)$: $L^p$-reverse H\"older inequality for the gradients of harmonic functions; (iii) $(R_p)$: $L^p$-boundedness of the Riesz transform ($p<\infty$); (iv) $(GBE)$: a generalised Bakry-\'Emery condition. We show that, for $p\in (2,\infty)$, (i), (ii) (iii) are equivalent, wh…
Geometry and analysis of Dirichlet forms
2012
Let $ \mathscr E $ be a regular, strongly local Dirichlet form on $L^2(X, m)$ and $d$ the associated intrinsic distance. Assume that the topology induced by $d$ coincides with the original topology on $ X$, and that $X$ is compact, satisfies a doubling property and supports a weak $(1, 2)$-Poincar\'e inequality. We first discuss the (non-)coincidence of the intrinsic length structure and the gradient structure. Under the further assumption that the Ricci curvature of $X$ is bounded from below in the sense of Lott-Sturm-Villani, the following are shown to be equivalent: (i) the heat flow of $\mathscr E$ gives the unique gradient flow of $\mathscr U_\infty$, (ii) $\mathscr E$ satisfies the Ne…
Vector-Valued Hardy Spaces
2019
Given a Banach space X, we consider Hardy spaces of X-valued functions on the infinite polytorus, Hardy spaces of X-valued Dirichlet series (defined as the image of the previous ones by the Bohr transform), and Hardy spaces of X-valued holomorphic functions on l_2 ∩ B_{c0}. The chapter is dedicated to study the interplay between these spaces. It is shown that the space of functions on the polytorus always forms a subspace of the one of holomorphic functions, and these two are isometrically isomorphic if and only if X has ARNP. Then the question arises of what do we find in the side of Dirichlet series when we look at the image of the Hardy space of holomorphic functions. This is also answer…
Simurgh
2021
The availability of non-volatile main memory (NVMM) has started a new era for storage systems and NVMM specific file systems can support extremely high data and metadata rates, which are required by many HPC and data-intensive applications. Scaling metadata performance within NVMM file systems is nevertheless often restricted by the Linux kernel storage stack, while simply moving metadata management to the user space can compromise security or flexibility. This paper introduces Simurgh, a hardware-assisted user space file system with decentralized metadata management that allows secure metadata updates from within user space. Simurgh guarantees consistency, durability, and ordering of updat…
Mapping daily global solar irradiation over Spain: A comparative study of selected approaches
2011
Abstract Three methods to estimate the daily global solar irradiation are compared: the Bristow–Campbell (BC), Artificial Neural Network (ANN) and Kernel Ridge Regression (KRR). BC is an empirical approach based on air maximum and minimum temperature. ANN and KRR are non-linear approaches that use temperature and precipitation data (which have been selected as the best combination of input data from a gamma test). The experimental dataset includes 4 years (2005–2008) of daily irradiation collected at 40 stations and temperature and precipitation data collected at 400 stations over Spain. Results show that the ANN method produces the best global solar irradiation estimates, with a mean absol…