Tensor Analysis with n-Mode Generalized Difference Subspace
Tensor Analysis with n-Mode Generalized Difference Subspace
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DOI：
10.1016/j.eswa.2020.114559
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
B. Gatto;E. M. Santos;Alessandro Lameiras Koerich;K. Fukui;Waldir S. S. Júnior-Waldir-S.-S.-Júnior-2041768594
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文献类型：
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作者：
B. Gatto;E. M. Santos;Alessandro Lameiras Koerich;K. Fukui;Waldir S. S. Júnior-Waldir-S.-S.-Júnior-2041768594
多个传感器的使用越来越多，产生了大量的多维数据，需要有效的表示和分类方法。在本文中，我们提出了一种新的多维数据分类方法，它依赖于两个前提：（1）多维数据通常由张量表示，因为这带来了多线性代数和建立张量因式分解方法的好处;（2）多线性数据可以由向量空间的子空间描述。子空间表示已被用于模式集识别，其张量表示对应物也可在文献中获得。然而，传统的方法没有利用张量的判别信息，降低了分类精度。在这种情况下，广义差子空间（GDS）提供了一个增强的子空间表示，减少数据冗余和揭示歧视性的结构。由于GDS不处理张量数据，我们提出了一个新的投影称为n模式GDS，有效地处理张量数据。我们还引入了当时模式的Fisher得分作为类可分性指标和改进的度量基于测地距离的张量数据的相似性。手势和动作识别的实验结果表明，该方法优于文献中常用的方法，而不依赖于预训练模型或迁移学习。
The increasing use of multiple sensors, which produce a large amount of multi-dimensional data, requires efficient representation and classification methods. In this paper, we present a new method for multi-dimensional data classification that relies on two premises: (1) multi-dimensional data are usually represented by tensors, since this brings benefits from multilinear algebra and established tensor factorization methods; and (2) multilinear data can be described by a subspace of a vector space. The subspace representation has been employed for pattern-set recognition, and its tensor representation counterpart is also available in the literature. However, traditional methods do not use discriminative information of the tensors, degrading the classification accuracy. In this case, generalized difference subspace (GDS) provides an enhanced subspace representation by reducing data redundancy and revealing discriminative structures. Since GDS does not handle tensor data, we propose a new projection calledn-mode GDS, which efficiently handles tensor data. We also introduce then-mode Fisher score as a class separability index and an improved metric based on the geodesic distance for tensor data similarity. The experimental results on gesture and action recognition show that the proposed method outperforms methods commonly used in the literature without relying on pre-trained models or transfer learning.