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半监督拉普拉斯特征映射算法 被引量：4

Semi-supervised Laplacian Eigenmap
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摘要 为了使流形学习方法具有半监督的特点,利用流形上某些已知低维信息的数据去学习推测出其它数据的低维信息,扩大流形学习算法的应用范围,把拉普拉斯特征映射算法(Laplacian Eigenmap,LE)与半监督的机器学习相结合,提出一种半监督的拉普拉斯特征映射算法(semi-supervised Laplacian Eigenmap,SSLE),这种半监督的流形学习算法在分类识别等问题上,具有很好的效果。模拟实验和实际例子都表明了SSLE算法的有效性。 How incorporate manifold learning and semi-supervised machine learning to extend the manifold learning algorithm.One way is to use the prior information in the form of on-manifold coordinates of certain data samples to compute the low-dimension coordinates of the other data samples.Combined Laplacian Eigenmap（LE） with semi-supervised machine learning,a semi-supervised Laplacian Eigenmap（SSLE） is presented.Simulation and real examples show that SSLE is more effective in clasaification and recognition field.
作者 刘海红 周聪辉
机构地区 华侨大学计算机科学与技术学院 香港中文大学计算机科学与工程学系
出处 《计算机工程与设计》 CSCD 北大核心 2012年第2期601-606,共6页 Computer Engineering and Design
基金 福建省自然科学基金项目(A0810013) 华侨大学科研基金项目(04BS313)
关键词 拉普拉斯特征映射算法 半监督机器学习 流形学习 低维信息 模式识别 Laplacian Eigenmap semi-supervised machine learning manifold learning low-dimensional information pattern recognize
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献15

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同被引文献60

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  • 5ZHAO Zeng-shun, ZHANG Li, ZHAO Meng, et al. Gabor face recog- nition by muhi-channel classifier fusion of supervised kernel maniiold learning[J]. Neuroeompuling,2012,97 (11) :398-404.
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  • 7MAHBUB U, IMTIAZ H, ROY T, et al. A template matching ap- proach of one-short-learning gesture recognition [ J ]. Pattern Recog- nition Letters,2013,34 ( 15 ) : 1780-1788.
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  • 9ZHAO Zeng-shun, FENG Xiang, LIN Yan-yan, et al. Improved Rao- Blackwellized particle filter by particle swarm optimization [ J ]. dour- hal of Applied Mathematics, 2013,2013:302170.
  • 10SCHWARZ L A, MATEUS D, CASTANEDA V, et al. Recognizing multiple human activities and tracking full-body pose in unconstrained environments [ J ]. Pattern Recog nition, 2012,45 ( 1 ) : 11 - 23.

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计算机工程与设计
2012年 第2期

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