Novel semisupervised high-dimensional correspondences learning method

Chenping Hou; Yi Wu; Dongyun Yi; Yuanyuan Jiao

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Novel semisupervised high-dimensional correspondences learning method

机译：一种新型的半监督高维对应学习方法

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摘要

Correspondence is one of the big challenges in machine learning and image processing. To match two high-dimensional data sets with a certain number of aligned training examples, a novel semisupervised method is proposed. It is mainly based on two manifold learning approaches: maximum variance unfolding (MVU) and locally linear embedding (LLE). We have modified MVU to a semi-supervised version to solve the correspondence problem. Additionally, the nonuniform warps and folds caused by employing LLE alone and the computational burden of MVU disappear when they are combined. The proposed algorithm outperforms traditional methods in accuracy and efficiency. Three examples are performed to demonstrate the potential of this method.

机译：通讯是机器学习和图像处理中的重大挑战之一。为了将两个高维数据集与一定数量的对齐训练实例进行匹配，提出了一种新颖的半监督方法。它主要基于两种流形学习方法：最大方差展开（MVU）和局部线性嵌入（LLE）。我们已经将MVU修改为半监督版本，以解决对应问题。此外，单独使用LLE导致的不均匀翘曲和折痕以及MVU的计算负担在组合在一起时就消失了。该算法在准确性和效率上均优于传统方法。进行了三个例子来证明这种方法的潜力。

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来源
《Optical engineering》 |2008年第4期|047201.1-047201.10|共10页
作者
Chenping Hou; Yi Wu; Dongyun Yi; Yuanyuan Jiao;
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作者单位

National University of Defense Technology Department of Mathematics and System Science College of Science Changsha 410073, P. R. China;

National University of Defense Technology Department of Mathematics and System Science College of Science Changsha 410073, P. R. China;

National University of Defense Technology Department of Mathematics and System Science College of Science Changsha 410073, P. R. China;

National University of Defense Technology Department of Mathematics and System Science College of Science Changsha 410073, P. R. China;

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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
correspondence; manifold learning; locally linear embedding; maximum variance unfolding;

机译：对应;综合学习;局部线性嵌入;最大方差展开;

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1. Learning high-dimensional correspondence via manifold learning and local approximation [J] . Chenping Hou, Feiping Nie, Hua Wang, Neural computing & applications . 2014,第7a8期

机译：通过流形学习和局部逼近学习高维对应
2. Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning [J] . Ahmadi Seyyed Ali, Mehrshad Nasser, Razavi Seyyed Mohammad The imaging science journal . 2018,第5a6期

机译：基于半监督学习和度量学习相结合的高光谱图像半监督降维
3. Semisupervised classification of remote sensing images using efficient neighborhood learning method [J] . Neeta S. Kothari, Saroj K. Meher Engineering Applications of Artificial Intelligence . 2020,第Apra期

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4. The Characteristic of Correspondence Analysis Estimator to Estimate Latent Variable Model Method Using High-Dimensional AIC [C] . Bambang Avip Priatna M, Lukman, dan Encum Sumiaty International Seminar on Mathematics, Science, and Computer Science Education . 2016

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5. Large-Scale and High-Dimensional Statistical Learning: Methods and Algorithms [D] . Qian, Junyang. 2020

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6. On Efficient Large Margin Semisupervised Learning: Method and Theory [O] . Junhui Wang, Xiaotong Shen, Wei Pan -1

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7. Extending SemiSupervised Learning Methods for Inductive Transfer Learning [O] . Yuan Shi, Zhenzhong Lan, Wei Liu, 2015

机译：扩展半监督学习方法的归纳学习

Novel semisupervised high-dimensional correspondences learning method

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