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Title:
基于图的半监督降维算法
Alternative Title: Graph-based Semi-supervised Dimensionality Reduction Algorithm
Author: 杨格兰 ; 金辉霞 ; 孟令中 ; 朱幸辉
Keyword: 半监督学习 ; 流形学习 ; 标记传播 ; 图谱理论 ; Semi-supervised learning ; Manifold learning ; Label propagation ; Spectral graph theory
Source: 计算机科学
Issued Date: 2014
Volume: 41, Issue:4, Pages:280-282,296
Indexed Type: CSCD
Department: 湖南城市学院信息科学与工程学院 益阳413000 湖南城市学院通信与电子工程学院 益阳413000 中国科学院软件研究所基础软件测评实验室 北京100190 湖南农业大学信息科学工程学院 长沙410128
Abstract: 非线性降维和半监督学习都是近年来机器学习的热点.将半监督的方法运用到非线性降维中,提出了基于图的半监督降维的算法.该算法用等式融合的方法推出了标记传播算法的另一种表达形式,用标记传播的结果作为初始的数据映射,然后在图谱张成的线性空间中寻找最逼近初始映射的数据作为最后的半监督降维的结果.实验表明,所提算法可以获得平滑的数据映射,更接近于理想的降维效果.与标记传播算法、图谱逼近算法、无监督的降维算法的比较也体现出本算法的优越性.
English Abstract: Nonlinear dimensionality reduction and semi-supervised learning are both hot issues in machine learning area. Based on semi-supervised method, the article solved nonlinear dimensionality reduction problem to make up for the shortfall of ordinary methods. By using integration of equalities, a novel expression of label propagation algorithm was proposed. We used the label propagation result as the initial value mapping, and then found the best approximation to it in the graph spectral space. The experiment shows that our semi-supervised dimensionality reduction method can achieve smooth data mapping that is closer to the ideal effect.
Language: 中文
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Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16753
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
杨格兰,金辉霞,孟令中,等. 基于图的半监督降维算法[J]. 计算机科学,2014-01-01,41(4):280-282,296.
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