[1]刘立月,黄兆华,刘遵雄.高维数据分类中的特征降维研究[J].江西师范大学学报(自然科学版),2012,(02):131-134.
　LIU Li-yue,HUANG Zhao-hua,LIU Zun-xiong.The Research on Dimensionality Reduction for High-Dimensional Data Classification[J].Journal of Jiangxi Normal University:Natural Science Edition,2012,(02):131-134.
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高维数据分类中的特征降维研究()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年02期
页码:
131-134
栏目:
出版日期:
2012-03-01

文章信息/Info

Title:
The Research on Dimensionality Reduction for High-Dimensional Data Classification
作者:
刘立月;黄兆华;刘遵雄
华东交通大学软件学院,江西南昌,330013；华东交通大学信息工程学院,江西南昌,330013
Author(s):
LIU Li-yue；HUANG Zhao-hua；LIU Zun-xiong
关键词:
高维数据; 降维; 特征抽取; 稀疏正则化
Keywords:
high-dimensional data;  dimension reduction;  feature extraction;  sparse regularization
分类号:
TP181
文献标志码:
A
摘要:
以高维分类为目标,从分类的准确率与模型解释性角度探讨了降维的必要性,分析了特征选择与抽取2类方法特点,并对常用的特征抽取方法,包括主成分分析(PCA)、偏最小二乘(PLS)和非负矩阵分解(NMF)进行了阐述.考虑到约减后的数据缺乏稀疏性与可解释性,提出了基于稀疏正则化的特征抽取模型,为高维特征降维提供了一种新思路.
Abstract:
With the goal of high-dimensional classification, dimension reduction is discussed from the perspective of classification accuracy and model interpretation, and feature selection and feature extraction characterizes are analyzed. This paper introduces common feature extraction methods, including Principal Component Analysis, Partial Least Squares and Nonnegative Matrix Factorization. Considering the reduced data lacking in sparseness and interpretation, a sparse regularization based feature extraction framework has been proposed, and it provides dimensionality reduction in high-dimensional space a novel and available approach.

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相似文献/References:

[1]聂斌,王卓,杜建强,等.基于偏最小二乘法的信息粒降维及聚类研究[J].江西师范大学学报(自然科学版),2012,(05):472.
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备注/Memo

备注/Memo:
国家自然科学基金(61065003;61165004)；江西省教育厅科学技术研究(GJJ12308)
更新日期/Last Update: 1900-01-01