Multidimensional extension of singular spectrum analysis based on filtering interpretation
Multidimensional extension of singular spectrum analysis based on filtering interpretation
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基于滤波解释的奇异谱分析的多维扩展
DOI：
10.1142/s1793536914500058
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Kenji Kume and Naoko Nose-Togawa
中科院分区：
文献类型：
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作者：
伊藤仁;伊藤彰則;伊藤仁;Masashi Ito;Kenji Kume and Naoko Nose-Togawa;Kenji Kume and Naoko Nose-Togawa
奇异谱分析是时间序列的非参数谱分解。奇异谱分析可以看作是由原始时间序列自适应构造的特征滤波器完备集的两步滤波。基于这一观点，我们提出了一种灵活而简单的奇异谱分析算法，可以应用于任意维数的多维数据序列。我们对二维图像数据进行了分解，发现最优构造的滤波器是各种类型的平滑滤波器或边缘增强滤波器。我们还研究了一个简单的3D数据分解示例。
Singular spectrum analysis is a nonparametric spectral decomposition of a time series. The singular spectrum analysis can be viewed as the two-step filtering with the complete set of eigenfilter adaptively constructed from the original time series. Based on this viewpoint, we present a flexible and quite simple algorithm for the singular spectrum analysis which can be applied to the multidimensional data series with arbitrary dimension. We have carried out the decomposition of two-dimensional image data, and the optimally constructed filters are found to be the smoothing or the edge enhancement filters of various type. We have also examined a simple example for the decomposition of 3D data.