Options for multimodal classification based on L1-Tucker decomposition
Options for multimodal classification based on L1-Tucker decomposition
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DOI：
10.1117/12.2520140
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Dimitrios Chachlakis;M. Dhanaraj;Ashley Prater-Bennette;Panos P. Markopoulos
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文献类型：
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作者：
Dimitrios Chachlakis;M. Dhanaraj;Ashley Prater-Bennette;Panos P. Markopoulos
最常用的分类算法以向量的形式处理数据。同时，现代数据集通常包括自然建模为多路阵列（也称为张量）的多模态测量。以其张量形式处理多路数据可以实现增强的推理和分类准确性。Tucker分解是张量数据处理的标准方法，然而，由于其L2范数公式，它对损坏的测量结果表现出严重的敏感性。在这项工作中，我们提出了一个选择的分类方法，采用L1-范数为基础的，抗腐败的重新制定塔克（L1-塔克）。我们在多个真实的数据集上的实验研究证实了L1-Tucker的抗腐蚀性和分类精度。
Most commonly used classification algorithms process data in the form of vectors. At the same time, mod- ern datasets often comprise multimodal measurements that are naturally modeled as multi-way arrays, also known as tensors. Processing multi-way data in their tensor form can enable enhanced inference and classification accuracy. Tucker decomposition is a standard method for tensor data processing, which however has demonstrated severe sensitivity to corrupted measurements due to its L2-norm formulation. In this work, we present a selection of classification methods that employ an L1-norm-based, corruption-resistant reformulation of Tucker (L1-Tucker). Our experimental studies on multiple real datasets corroborate the corruption-resistance and classification accuracy afforded by L1-Tucker.