# Common Mode Patterns for Supervised Tensor Subspace Learning

## TL;DR

This paper introduces Common Mode Patterns, a supervised tensor subspace learning method that enhances class separation by considering label information during dimensionality reduction, validated on hyperspectral data.

## Contribution

It presents a novel supervised tensor subspace learning technique that improves class separability compared to existing methods like Multilinear PCA.

## Key findings

- CMP effectively reduces tensor dimensionality
- Increases inter-class separability
- Outperforms Multilinear PCA on hyperspectral data

## Abstract

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.