Journal of Guangxi Normal University(Natural Science Edition) ›› 2011, Vol. 29 ›› Issue (3): 131-135.

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Algorithm of Supervised Learning on Outlier Manifold

HUANG Tian-qiang, LI Kai, ZHENG Zhi   

  1. Department of Computer Science,School of Mathematics and Computer Science,Fujian Normal University,Fuzhou 350007,China
  • Received:2011-06-05 Online:2011-08-20 Published:2018-12-03

Abstract: Manifold learning algorithm is an important tool in the field of dimension reduction and data visualization.Improving the algorithm's efficiency and robustness is of positive significance to its practical application.Classical manifold learning algorithm is sensitive to noise points,and its improved algorithms have been imperfect.This paper presents a robust manifold learningalgorithm based on supervised learning and kernel function.It introduces nuclearmethods and supervised learning into the dimensionality reduction,and takes fulladvantage of the label of some data and the property of kernel function.The proposed algorithm can make close and same types of samples and distribute different types of samples,thus to improves the effect of the classification task and reduce the noise sensitivity of outliers on manifold.The experiments on the UCI data and Raman data of leukemia reveal that the algorithm has better noise immunity.

Key words: manifold learning, supervised learning, kernel function

CLC Number: 

  • TP309
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