  In Cultural Heritage, hyperspectral images are commonly used since they
provide extended information regarding the optical properties of materials.
Thus, the processing of such high-dimensional data becomes challenging from the
perspective of machine learning techniques to be applied. In this paper, we
propose a Rank-$R$ tensor-based learning model to identify and classify
material defects on Cultural Heritage monuments. In contrast to conventional
deep learning approaches, the proposed high order tensor-based learning
demonstrates greater accuracy and robustness against overfitting. Experimental
results on real-world data from UNESCO protected areas indicate the superiority
of the proposed scheme compared to conventional deep learning models.
