核希尔伯特再生空间
子空间拓扑
正规化(语言学)
域适应
歧管(流体力学)
算法
核(代数)
数学
歧管对齐
希尔伯特空间
计算机科学
降维
人工智能
支持向量机的正则化研究进展
领域(数学分析)
模式识别(心理学)
非线性降维
反问题
离散数学
数学分析
Tikhonov正则化
分类器(UML)
工程类
机械工程
作者
Xi Liu,Zehui Zhan,Junying Yuan
标识
DOI:10.1109/icairc52191.2021.9544928
摘要
Domain adaptation for classification is often encountered in recent years. A popular approach consists in transforming the source and target data to an identical linear space. Then the Maximum Mean Discrepancy (MMD) is used to evaluate the dissimilarity of distributions. However, the MMD only makes the source and target domain distribution consistent according to the global probability distribution, and cannot effectively protect the local geometric structure of the data. To make better use of the structure of local geometry, this paper proposes a method called domain adaptation based on manifold regularization (DAMR). First, this algorithm embeds the input data into a reproducing kernel Hilbert space (RKHS). Second, subspace-based dimensionality reduction is conducted on the RKHS. Third, a manifold regularization term is added to the learning method. Furthermore, the classification experiments demonstrate that DAMR is an accurate and effective method.
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