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 called n-mode GDS, which efficiently handles tensor data. We also introduce the n-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.
翻译:多传感器的日益使用产生大量多维数据,需要高效的表述和分类方法。在本文中,我们提出了一个基于两个前提的新的多维数据分类方法,即:(1)多维数据通常由数以数代代表,因为这样可以带来多线变代数和既定的强度因子化方法的惠益;(2)多线数据可以通过矢量空间的子空间描述。次空间表示用于模式定型识别,文献中也提供对应的强度代表。然而,传统方法并不使用强度偏差信息,降低分类准确性。在这种情况下,普遍差异子空间(GDS)通过减少数据冗余和披露歧视结构,提供了强化的次空间代表。由于GDS不处理高压数据,我们建议采用称为n-mode GDS的新预测,以高效处理高压数据。我们还将n-mode Fisheral分数作为等级分离指数,并改进了基于索尔数据相似性地球偏差距离的衡量标准。在这种情况下,通用差异子空间子空间子空间(GDS)通过减少数据冗余和暴露前识别模型的实验结果,显示采用的方法。