Results 211 to 220 of about 939,081 (265)

Assessing Grasp Quality using Local Sensitivity Analysis

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
We 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.
Zechmair, Michael, Morel, Yannick
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Two-dimensional locality sensitive discriminant analysis

2008 International Conference on Wavelet Analysis and Pattern Recognition, 2008
Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images.
null Yantao Wei, null Hong Li, null Tian Xia   +2 more
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Local Sensitivity Analysis in Estimation Problems

Journal of Computational and Graphical Statistics, 2008
This 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, Carmen Castillo, Ali S Hadi, Jose Maria Sarabia   +3 more
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Local Sensitivity Analysis

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*, C Srinivasan**, L Wasserman*   +2 more
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Stable locality sensitive discriminant analysis for image recognition

Neural Networks, 2014
Locality Sensitive Discriminant Analysis (LSDA) is one of the prevalent discriminant approaches based on manifold learning for dimensionality reduction. However, LSDA ignores the intra-class variation that characterizes the diversity of data, resulting in unstableness of the intra-class geometrical structure representation and not good enough ...
Gao, Quanxue, Liu, Jingjing, Cui, Kai, Zhang, Hailin, Wang, Xiaogang   +4 more
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TENSOR LOCALITY SENSITIVE DISCRIMINANT ANALYSIS AND ITS COMPLEXITY

International Journal of Wavelets, Multiresolution and Information Processing, 2009
Feature 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
Wei, Yantao, Li, Hong, Li, Luoqing
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Uncorrelated Locality-Sensitive Multi-view Discriminant Analysis

National Academy Science Letters, 2020
Recently, multi-view feature learning technique has attracted lots of research interest. Discriminant analysis-based multi-view feature learning is an important research branch. Although some multi-view discriminant analysis methods have been presented, there still exists room for improvement.
Fei Wu, Xiao-Yuan Jing, Qinghua Huang
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Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach

Biometrics, 2001
Summary.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, Molenberghs, Geert, Thijs, Herbert, Lesaffre, Emmanuel, Kenward, Michael G.   +4 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), 2016
In 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, Weicheng Cai, Zhidong Ni, Ming Li   +3 more
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