有监督的噪音流形学习算法

广西师范大学学报（自然科学版） ›› 2011, Vol. 29 ›› Issue (3): 131-135.

有监督的噪音流形学习算法

黄添强, 李凯, 郑之

福建师范大学计算机科学与技术学院计算机科学系,福建福州350007

收稿日期:2011-06-05 出版日期:2011-08-20 发布日期:2018-12-03
通讯作者: 黄添强(1971—),男,福建莆田人,福建师范大学副教授,博士。E-mail:fjhtq@fjnu.edu.cn
基金资助:
国家自然科学基金资助项目(61070062);福建省自然科学基金资助项目(2008J04004);福建省高校服务海西建设重点项目(2008HX200941-4-5)

Algorithm of Supervised Learning on Outlier Manifold

HUANG Tian-qiang, LI Kai, ZHENG Zhi

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

摘要/Abstract

摘要： 流形学习算法是维度约简与数据可视化领域的重要工具,提高算法的效率与健壮性对其实际应用有积极意义。经典的流形学习算法普遍的对噪音点较为敏感,现有的改进算法尚存在不足。本文提出一种基于监督学习与核函数的健壮流形学习算法,把核方法与监督学习引入降维过程,利用已知标签数据信息与核函数特性,使得同类样本变得紧密,不同类样本变成分散,提高后续分类任务的效果,降低算法对流形上噪音的敏感性。在UCI数据与白血病拉曼光谱数据上的实验表明本文改进的算法具有更高的抗噪性。

关键词: 流形学习, 监督学习, 核函数

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

中图分类号:

TP309

黄添强, 李凯, 郑之. 有监督的噪音流形学习算法[J]. 广西师范大学学报（自然科学版）, 2011, 29(3): 131-135.

HUANG Tian-qiang, LI Kai, ZHENG Zhi. Algorithm of Supervised Learning on Outlier Manifold[J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(3): 131-135.

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