Conformity evaluation and L1-norm principal-component analysis of tensor data
Conformity evaluation and L1-norm principal-component analysis of tensor data
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DOI：
10.1117/12.2520538
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发表时间：
2019-05
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影响因子：
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通讯作者：
Konstantinos Tountas;D. Pados;M. Medley
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文献类型：
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作者：
Konstantinos Tountas;D. Pados;M. Medley
多模式张量数据集在现代科学和工程应用中的频率越来越高，例如在生物医学科学和自主工程系统中。在过去的二十年中，张量域数据分析主要是在张量域上的标准(L2范数)特征向量分解的背景下进行的。这些算法不是联合张量域最优的，并且表现出所有L2范数主成分分析方法所特有的对错误/损坏/缺失测量的常见敏感性。在这项工作中，我们提出了一种健壮化的方法来评估张量数据条目相对于整个可访问数据集的一致性。符合性评估基于计算的L1范数张量子空间的不断细化的序列。理论发展是在多传感器定位应用的背景下说明的，它显示出前所未有的估计性能和对间歇性干扰的抵抗能力。文中还给出了一个脑电数据分析实验。
Multi-modal tensor data sets arise with increasing frequency in modern day scientific and engineering applications, for example in biomedical sciences and autonomous engineered systems. Over the past twenty years, tensor-domain data analysis has been attempted primarily in the context of standard (L2-norm) eigenvector decompositions across tensor domains. The algorithms are not joint-tensor-domain optimal and exhibit the familiar sensitivity to faulty/corrupted/missing measurements that characterizes all L2-norm principal-component analysis methods. In this work, we present a robustified method to evaluate the conformity of tensor data entries with respect to the whole accessible data set. Conformity evaluation is based on a continuously refined sequence of calculated L1norm tensor subspaces. The theoretical developments are illustrated in the context of a multisensor localization application that indicates unprecedented estimation performance and resistance to intermittent disturbances. An electroencephalogram (EEG) data analysis experiment is also presented.