Projection-free kernel principal component analysis for denoising

Anh Tuan Bui; Im Joon-Ku; Apley Daniel W.; Runger George C.

首页> 外文期刊>Neurocomputing >Projection-free kernel principal component analysis for denoising

【24h】

Projection-free kernel principal component analysis for denoising

机译：脱色的非预测内核主成分分析

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Kernel principal component analysis (KPCA) forms the basis for a class of methods commonly used for denoising a set of multivariate observations. Most KPCA algorithms involve two steps: projection and preimage approximation. We argue that this two-step procedure can be inefficient and result in poor denoising. We propose an alternative projection-free KPCA denoising approach that does not involve the usual projection and subsequent preimage approximation steps. In order to denoise an observation, our approach performs a single line search along the gradient descent direction of the squared projection error. The rationale is that this moves an observation towards the underlying manifold that represents the noiseless data in the most direct manner possible. We demonstrate that the approach is simple, computationally efficient, robust, and sometimes provides substantially better denoising than the standard KPCA algorithm. (C) 2019 Elsevier B.V. All rights reserved.

机译：内核主成分分析（KPCA）构成了一类常用于去噪的一组多变量观测的方法的基础。大多数KPCA算法涉及两个步骤：投影和预测近似。我们认为这两步程序可能效率低，导致不良的去噪。我们提出了一种替代的无预测KPCA去噪方法，不涉及通常的预测和随后的预测近似步骤。为了去代标观察，我们的方法沿着平方投影误差的梯度下降方向执行单线搜索。基本原理是，这对底层歧管的观察移动到最直接的方式代表无噪声数据。我们证明该方法简单，计算效率，稳健，有时比标准KPCA算法提供了基本更好的去噪。（c）2019 Elsevier B.v.保留所有权利。

著录项

来源
《Neurocomputing》 |2019年第10期|163-176|共14页
作者
Anh Tuan Bui; Im Joon-Ku; Apley Daniel W.; Runger George C.;
展开▼
作者单位

Northwestern Univ Dept Ind Engn & Management Sci 2145 Sheridan Rd Evanston IL 60208 USA;

Anthem Inc 233 South Wacker Dr Suite 3700 Chicago IL 60606 USA;

Northwestern Univ Dept Ind Engn & Management Sci 2145 Sheridan Rd Evanston IL 60208 USA;

Arizona State Univ Sch Comp Informat & Decis Syst Engn 699 S Mill Ave Tempe AZ 85281 USA;

展开▼
收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Image processing; Feature space; Pattern recognition; Preimage problem;

机译：图像处理;特征空间;模式识别;预报问题;

相似文献

外文文献
中文文献
专利

1. Projection-free kernel principal component analysis for denoising [J] . Anh Tuan Bui, Im Joon-Ku, Apley Daniel W., Neurocomputing . 2019,第SEPa10期

机译：去噪的无投影核主成分分析
2. Nonlinear denoising and analysis of neuroimages with kernel principal component analysis and pre-image estimation [J] . RasmussenP.M., AbrahamsenT.J., MadsenK.H., NeuroImage . 2012,第3期

机译：神经核的非线性降噪与核主成分分析和图像前估计
3. Tangent Hyperplane Kernel Principal Component Analysis for Denoising [J] . Im J.-K., Apley D. W., Runger G. C. Neural Networks and Learning Systems, IEEE Transactions on . 2012,第4期

机译：切线超平面核主成分分析
4. COMPREHENSIVE EVALUATION OF PARALLEL MECHANISM AND ROBOT PERFORMANCE BASED ON PRINCIPAL COMPONENT ANALYSIS AND KERNEL PRINCIPAL COMPONENT ANALYSIS [C] . Liming Li, Jing Zhao Mechanisms and robotics conference;ASME international design engineering technical conferences and computers and information in engineering conference . 2016

机译：基于主成分分析和核主成分分析的并行机制和机器人性能综合评价
5. Efficient Clustering via Kernel Principal Component Analysis and Optimal One-Dimensional Thresholding [D] . Bhide, Nachiket. 2021

机译：通过内核主体成分分析和最佳一维阈值的高效群集
6. A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI [O] . Nan-kuei Chen, Hing-Chiu Chang, Ali Bilgin, -1

机译：基于扩散匹配主成分分析（DM-PCA）的两通道降噪程序用于高分辨率扩散加权MRI
7. Fast kernel Fisher discriminant analysis via approximating the kernel principal component analysis [O] . Wang J, Li Q, You J, 2011

机译：通过近似内核主成分分析来快速进行Fisher Fisher判别分析

Projection-free kernel principal component analysis for denoising

摘要

著录项

相似文献

相关主题

期刊订阅