Multi-label classification based on singular value decomposition-partial least squares regression

doi:10.11772/j.issn.1001-9081.2014.07.2058

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (7): 2058-2060.DOI: 10.11772/j.issn.1001-9081.2014.07.2058

• Artificial intelligence • Previous Articles Next Articles

Multi-label classification based on singular value decomposition-partial least squares regression

MA Zongjie¹,LIU Huawen¹,²

1. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua Zhejiang 321004, China;
2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100055, China

Received:2013-12-10 Revised:2014-01-30 Online:2014-08-01 Published:2014-07-01
Contact: LIU Huawen

基于奇异值分解—偏最小二乘回归的多标签分类算法

马宗杰¹,刘华文¹,²

1. 浙江师范大学数理与信息工程学院，浙江金华 321004
2. 中国科学院数学与系统科学研究院，北京 100055

通讯作者: 刘华文
作者简介:马宗杰(1986-),男,河北石家庄人,硕士研究生,主要研究方向:多标签分类、模式识别;刘华文(1977-),男,江西抚州人,副教授,博士,主要研究方向:特征选择、模式识别、多标签分类。
基金资助:
国家自然科学基金资助项目;中国博士后科学基金资助项目;模式识别国家重点实验室开放基金资助项目;浙江省自然科学基金资助项目

Abstract

Abstract:

To tackle multi-label data with high dimensionality and label correlations, a multi-label classification approach based on Singular Value Decomposition (SVD)-Partial Least Squares Regression (PLSR) was proposed, which aimed at performing dimensionality reduction and regression analysis. Firstly, the label space was taken into a whole so as to exploit the label correlations. After that, the score vectors of both the instance space and label space were obtained by SVD, which was used for dimensionality reduction. Finally, the model of multi-label classification was established based on PLSR. The experiments performed on four real data sets with higher dimensionality verify the effectiveness of the proposed method.

摘要：

针对多标签数据的标签相关性和高维问题，提出一种基于奇异值分解—偏最小二乘回归的多标签分类算法，该算法可以对多标签数据进行维数约简和回归分析。首先，将类别标签集合作为整体处理,对标签相关性进行考察; 其次，利用奇异值分解(SVD)技术得到样本和标签空间的得分向量，实施降维; 最后，在偏最小二乘回归(PLSR)的基础上构建多标签分类模型。实验结果表明，在四种维数较高的真实数据集上，该算法可以获得有效的分类结果。

CLC Number:

TP181

MA Zongjie LIU Huawen. Multi-label classification based on singular value decomposition-partial least squares regression[J]. Journal of Computer Applications, 2014, 34(7): 2058-2060.

马宗杰刘华文. 基于奇异值分解—偏最小二乘回归的多标签分类算法[J]. 计算机应用, 2014, 34(7): 2058-2060.

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Multi-label classification based on singular value decomposition-partial least squares regression

基于奇异值分解—偏最小二乘回归的多标签分类算法

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