Search results for "Kernel Density Estimation."
showing 4 items of 34 documents
Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis
2014
This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. Th…
A family of kernel anomaly change detectors
2014
This paper introduces the nonlinear extension of the anomaly change detection algorithms in [1] based on the theory of reproducing kernels. The presented methods generalize their linear counterparts, under both the Gaussian and elliptically-contoured assumptions, and produce both improved detection accuracies and reduced false alarm rates. We study the Gaussianity of the data in Hilbert spaces with kernel dependence estimates, provide low-rank kernel versions to cope with the high computational cost of the methods, and give prescriptions about the selection of the kernel functions and their parameters. We illustrate the performance of the introduced kernel methods in both pervasive and anom…
Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel
2008
This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.
L'évolution de l'occupation humaine : l'analyse spatiale exploratoire des données.Le problème de l'incertitude et de l'hétérogénéité des données en a…
2011
The analysis of the evolution of the settlement requires a lot of methodological questioning. Despite a frequent use of the spatial analysis methods, few works return to their methodological problems. This article proposes to make a comparison and a discussion around two exploratory statistical methods (the K function of Ripley and the Kernel Density Estimation). It seems that scale, quality and quantity of input data, are three essential parameters to be taken into account so as to lead a spatial analysis in archaeology. In intrinsic way, archaeological data are non homogeneous. For that purpose, our article proposes a multi scalar approach integrating the non homogeneousness character of …