Search results for "Cook's distance"

showing 3 items of 3 documents

Efficient Kernel Cook's Distance for Remote Sensing Anomalous Change Detection

2021

Detecting anomalous changes in remote sensing images is a challenging problem, where many approaches and techniques have been presented so far. We rely on the standard field of multivariate statistics of diagnostic measures, which are concerned about the characterization of distributions, detection of anomalies, extreme events, and changes. One useful tool to detect multivariate anomalies is the celebrated Cook's distance. Instead of assuming a linear relationship, we present a novel kernelized version of the Cook's distance to address anomalous change detection in remote sensing images. Due to the large computational burden involved in the direct kernelization, and the lack of out-…

Atmospheric ScienceMultivariate statisticsComputer scienceMultispectral image0211 other engineering and technologies02 engineering and technology010501 environmental sciences01 natural sciencesField (computer science)13. Climate actionKernel (statistics)KernelizationLeverage (statistics)Computers in Earth SciencesCook's distanceChange detection021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing
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Nonlinear Cook distance for Anomalous Change Detection

2020

In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach. A regressor between images is used to discover the most {\em influential points} in the observed data. Typically, the pixels with largest residuals are decided to be anomalous changes. In order to find the anomalous pixels we consider the Cook distance and propose its nonlinear extension using random Fourier features as an efficient nonlinear measure of impact. Good empirical performance is shown over different multispectral images both visually and quantitatively evaluated with ROC curves.

FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceComputer Vision and Pattern Recognition (cs.CV)Multispectral imageComputer Science - Computer Vision and Pattern Recognition0211 other engineering and technologies02 engineering and technologyMeasure (mathematics)Machine Learning (cs.LG)Kernel (linear algebra)symbols.namesake0502 economics and businessCook's distance021101 geological & geomatics engineering050208 financePixelbusiness.industry05 social sciencesPattern recognitionNonlinear systemFourier transformKernel (image processing)Computer Science::Computer Vision and Pattern RecognitionsymbolsArtificial intelligencebusinessChange detection
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Outlier recognition in crystal-structure least-squares modelling by diagnostic techniques based on leverage analysis.

2005

The identification of the actual outliers in a least-squares crystal-structure model refinement and their subsequent elimination from the data set is a non-trivial task that has to be carried out carefully when a high level of accuracy of the estimates is required. One of the most suitable tools for detecting the influence of each data entry on the regression is the identification of ;leverage points'. On the other hand, the recognition of the actual statistical outliers is effectively possible by using some diagnostics as a function of the leverage, such as Cook's distance, DFFITS and FVARATIO. The evaluation of these estimators makes it possible to achieve a reliable identification of the…

Model refinementComputer scienceEstimatorcomputer.software_genreRegressionleast squareData pointCook's distanceleverage analysisStructural BiologyDFFITSOutliercrystal structure refinementLeverage (statistics)Data miningCook's distanceAlgorithmcomputerActa crystallographica. Section A, Foundations of crystallography
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