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On The Local Sensitivity Analysis for Phase Expansion
2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S), 2021This paper presents the local sensitivity for phase expansion. The purpose of phase expansion is to determine the phase-type (PH) parameters to approximate the original distribution with the fitted PH distribution. Since PH parameters are estimated from the original distribution, whose parameters may contain estimation errors, it is important to ...
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Assessing Grasp Quality using Local Sensitivity Analysis
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021We propose a new approach to investigate and quantify dynamic grasp performance. Oftentimes, existing approaches to grasp analysis assess a grasp's quality in a static situation. We build upon such considerations to also account for the dynamic nature of most grasp operations.
Michael Zechmair, Yannick Morel
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1996
Abstract We review several methods for assessing the effect of small changes to the prior distribution. Our emphasis is on a variety of derivative-like quantities. Some of these have deficiencies that make them unsuitable as diagnostics. We explore the reasons for this and we look at some attempts to avoid these problems.
P Gustafson* +2 more
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Abstract We review several methods for assessing the effect of small changes to the prior distribution. Our emphasis is on a variety of derivative-like quantities. Some of these have deficiencies that make them unsuitable as diagnostics. We explore the reasons for this and we look at some attempts to avoid these problems.
P Gustafson* +2 more
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Locality sensitive discriminant analysis for speaker verification
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016In this paper, we apply Locality Sensitive Discriminant Analysis (LSDA) to speaker verification system for intersession variability compensation. As opposed to LDA which fails to discover the local geometrical structure of the data manifold, LSDA finds a projection which maximizes the margin between i-vectors from different speakers at each local area.
Danwei Cai +3 more
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Analysis of local sensitivity to nonignorability with missing outcomes and predictors
Biometrics, 2021AbstractThe ISNI (index of sensitivity to local nonignorability) method quantifies local sensitivity of parametric inferences to nonignorable missingness in an outcome variable. Here we extend ISNI to the situations where both outcomes and predictors can be missing and where the missingness mechanism can be either parametric or semi‐parametric.
Heng Chen, Daniel F. Heitjan
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TENSOR LOCALITY SENSITIVE DISCRIMINANT ANALYSIS AND ITS COMPLEXITY
International Journal of Wavelets, Multiresolution and Information Processing, 2009Feature extraction is one of the most challenging problems in pattern recognition fields and has attracted great attention recently. In this paper, we propose a novel feature extraction algorithm named tensor locality sensitive discriminant analysis which accepts tensors as inputs. The algorithm preserves the key structure of data by using the labeled
Yantao Wei, Hong Li 0009, Luoqing Li
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Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach
Biometrics, 2001Summary.Diggle and Kenward (1994,Applied Statistics43, 49–93) proposed a selection model for continuous longitudinal data subject to nonrandom dropout. It has provoked a large debate about the role for such models. The original enthusiasm was followed by skepticism about the strong but untestable assumptions on which this type of model invariably rests.
Verbeke, Geert +4 more
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Local Sensitivity Analysis in Estimation Problems
Journal of Computational and Graphical Statistics, 2008This article deals with the problem of local sensitivity analysis, that is, how sensitive are the results of a statistical analysis to changes in the data? A general methodology of sensitivity analysis is applied to some statistical problems. The proposed methods are applicable to any statistical problem that can be expressed as an optimization problem
Enrique Castillo +3 more
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A locally sensitive method for cluster analysis
Pattern Recognition, 1976Abstract In this paper a new method of mode separation is proposed. The method is based on mapping of data points from the N -dimensional space onto a sequence so that the majority of points from each mode become successive elements of the sequence. The intervals of points in the sequence belonging to the respective modes of the p.d.f.
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Fast Object Localization via Sensitivity Analysis
2019Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data. Methods for object localization, however, are still in need of substantial improvement.
Mohammad K. Ebrahimpour, David C. Noelle
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