期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

一种快速的Isomap算法 被引量：2

A Fast Isomap Algorithm
原文传递
导出
摘要 针对Isomap采用Floyd-Warshall算法求最短路径时运算速度慢的问题,考虑到邻域图的稀疏性,提出了Isomap的改进算法.通过采用基于Fibonacci堆的Dijkstra算法,减少了求最短路径的时间,从而提高了Isomap的速度.在多个数据集上的实验结果表明,改进后的算法较原Isomap算法的运算速度快. For the slow operational speed problem of the Isomap algorithm in which the Floyd-Warshall algorithm is applied to finding shortest paths, an improved Isomap algorithm is proposed based on the sparseness of the adjacency graph. In the improved algorithm, the runtime for shortest paths is reduced by using Dijkstra＇ s algorithm based on a Fibonacci heap, thus speeding up the Isomap operation. The experimental results on several data sets show that the improved version of Isomap is faster than the original one.
作者 屈太国 蔡自兴
机构地区 中南大学信息科学与工程学院
出处 《信息与控制》 CSCD 北大核心 2014年第4期476-482,489,共8页 Information and Control
基金 国家自然科学基金资助项目(90820302 60805027) 教育部博士点基金资助项目(200805330005)
关键词 流形学习 ISOMAP FIBONACCI 堆 最短路径 DIJKSTRA 算法 manifold learning Isomap Fibonacci heap shortest path Dijkstra＇s algorithm
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献30

  • 1Data sets for nonlinear dimensionality reduction[ DB]. [2013 -06 -09]. http://isomap, stanford, edu/datasets, html.
  • 2Jolliffe I T. Principal component analysis[ M]. 2nd ed. New York, USA: Springer-Verlag, 2002.
  • 3Cox T F, Cox M AA. Multidimensional scaling[ M]. 2nd ed. London, UK: Chapman & Hall/CRC, 2001.
  • 4Zhang Z Y, Zha H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment [ J ]. SIAM Journal of Scientific Computing, 2004, 26(1 ): 313-338.
  • 5Tenenbaum J B, de Silva V, Langford J. A global geometric framework for nonlinear dimensionality reduction [ J ]. Science, 2000, 290 (5500) : 2319 -2323.
  • 6Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[ J]. Science, 2000, 290 (5500) : 2323 -2326.
  • 7Belkin M, Niyogi P. Laplacian eigen maps for dimensionality reduction and data representation [ J ]. Neural Computation, 2003, 15 (6) : 1373 - 1396.
  • 8Donoho D L, Grimes C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data[J]. Proceedings of the National Academy of Sciences, 2003, 100(10) : 5591 -5596.
  • 9Seung H S, Daniel D L. The manifold ways of perception[J]. Science, 2000, 290(5500) : 2268 -2269.
  • 10He X F, Yan S C, ttu Y X, et al. Face recognition using Laplacian faces[ J]. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2005, 27 (3) : 328 - 340.

二级参考文献66

  • 1肖应旺,徐保国.改进PCA在发酵过程监测与故障诊断中的应用[J].控制与决策,2005,20(5):571-574. 被引量：17
  • 2罗四维,赵连伟.基于谱图理论的流形学习算法[J].计算机研究与发展,2006,43(7):1173-1179. 被引量：76
  • 3Turk M, Pentland A. Eigenfaces for recognition[J]. J of Cognitive Neuroscience, 1991, 3(1): 71-86.
  • 4Amari S I, Cichocki A, Yang H. A new learning algorithm for blind source separation[C]. Advances in Neural Information Processing Systems. Cambridge: MIT Press, 1996: 757-763.
  • 5Cox T, Cox M. Multidimensional scaling[M]. London: Chapman and Hall, 1994.
  • 6Duda R O, Hart P E, Stork D G. Pattern classification[M]. 2nd ed. Beijing: China Machine Press, 2004.
  • 7Kohonen T. Self-organizing maps[M]. 3rd ed. Berlin: , Springer-Verlag, 2001.
  • 8Smola A J, Mika S, SchOlkopf B, et al. Regularized principal manifolds[J]. J of Machine Learning Research, 2001, 1(3): 179-209.
  • 9Bishop C M, Svensen M, Williams C K I. GTM: The generative topographic mapping[J]. Neural Computation, 1998, 10(1): 215-234.
  • 10Yang J, Frangi A E Yang J Y, et al. KPCA plus LDA: A complete kernel fisher discriminant framework for feature extraction and recognition[J]. IEEE TPAMI, 2005, 27(2): 230-244.

共引文献47

同被引文献16

引证文献2

二级引证文献4

信息与控制
2014年 第4期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部