  In this work we propose a method for reducing the dimensionality of tensor
objects in a binary classification framework. The proposed Common Mode Patterns
method takes into consideration the labels' information, and ensures that
tensor objects that belong to different classes do not share common features
after the reduction of their dimensionality. We experimentally validate the
proposed supervised subspace learning technique and compared it against
Multilinear Principal Component Analysis using a publicly available
hyperspectral imaging dataset. Experimental results indicate that the proposed
CMP method can efficiently reduce the dimensionality of tensor objects, while,
at the same time, increasing the inter-class separability.
