Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (1): 239-243.DOI: 10.11772/j.issn.1001-9081.2014.01.0239
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DONG Hongyu,CHEN Xiaoyun
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董红玉,陈晓云
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基金资助:
国家自然科学基金资助项目;福建省优秀人才支持计划项目
Abstract: The existing multivariate time series classification algorithms require sequences of equal length and neglect categories information. In order to solve these defects, a multivariate time series classification algorithm was proposed based on Singular Value Decomposition (SVD) and discriminant locality preserving projection. Based on the idea of dimension reduction, the first right singular vector of samples by SVD was used as feature vector to transform unequal length sequence into a sequence of identical size. Then the feature vector was projected by utilizing discriminant locality preserving projection based on maximum margin criterion, which made full use of categories information to ensure samples of the same class as close as possible and heterogeneous samples as dispersed as possible. Finally, it achieved the classification in a low dimension subspace by using 1 Nearest Neighbor (1NN), Parzen windows, Support Vector Machine (SVM) and Naive Bayes classifier. Experiments were carried out on Australian Sign Language (ASL), Japanese Vowels (JV) and Wafer, the three public multivariate time series datasets. The results show that the proposed algorithm achieves lower classification error rate under the condition of the same time complexity basically.
Key words: multivariate time series, classification, Singular Value Decomposition (SVD), Discriminant Locality Preserving Projection (DLPP), maximum margin criterion
摘要: 针对现有多变量时间序列分类算法存在的要求序列等长和忽视类别信息两个不足,提出基于奇异值分解(SVD)和判别局部保持投影的分类算法。该算法基于降维思想,先通过SVD将样本的第一右奇异向量作为特征向量,以此将不等长序列转化为规模大小相同的序列;接着采用基于最大间距准则的判别局部保持投影对特征向量投影,充分利用类别信息以确保投影后同类样本尽量接近,异类样本尽量分散;最后在低维子空间采用1最近邻(1NN)、Parzen窗、支持向量机(SVM)和朴素Bayes分类器进行分类。在Australian Sign Language(ASL)、Japanese Vowels(JV)和Wafer三个公开的多变量时间序列数据集上进行的实验结果表明:在时间开销基本不变的前提下,所提方法取得了较低的分类错误率。
关键词: 多变量时间序列, 分类, 奇异值分解, 判别局部保持投影, 最大间距准则
CLC Number:
TP311
DONG Hongyu CHEN Xiaoyun. Classification of multivariate time series based on singular value decomposition and discriminant locality preserving projection[J]. Journal of Computer Applications, 2014, 34(1): 239-243.
董红玉 陈晓云. 基于奇异值分解和判别局部保持投影的多变量时间序列分类[J]. 计算机应用, 2014, 34(1): 239-243.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2014.01.0239
http://www.joca.cn/EN/Y2014/V34/I1/239