Classification of multivariate time series using two-dimensional singular value decomposition
Classification of multivariate time series using two-dimensional singular value decomposition
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DOI：
10.1016/j.knosys.2008.03.014
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发表时间：
2008-10
期刊：
影响因子：
--
通讯作者：
Xiaoqing Weng;Junyi Shen
中科院分区：
文献类型：
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作者：
Xiaoqing Weng;Junyi Shen
多元时间序列（MTS）在多媒体、医学、金融和语音识别等领域有着广泛的应用。提出了一种基于二维奇异值分解（2dSVD）的MTS分类新方法。2dSVD是标准SVD的扩展，它明确地捕获了MTS样本的二维特性。计算MTS样本的行-行和列-列协方差矩阵的特征向量进行特征提取。在得到每个MTS样本的特征矩阵后，使用一近邻分类器进行MTS分类。在五个真实数据集上进行的实验结果证明了我们提出的方法的有效性。
Multivariate time series (MTS) are used in very broad areas such as multimedia, medicine, finance and speech recognition. A new approach for MTS classification using two-dimensional singular value decomposition (2dSVD) is proposed. 2dSVD is an extension of standard SVD, it captures explicitly the two-dimensional nature of MTS samples. The eigenvectors of row–row and column–column covariance matrices of MTS samples are computed for feature extraction. After the feature matrix is obtained for each MTS sample, one-nearest-neighbor classifier is used for MTS classification. Experimental results performed on five real-world datasets demonstrate the effectiveness of our proposed approach.