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.
View on arXiv