Topological Hyperbolic Representation For Classification of Hyperspectral Images

In this work, we demonstrate the use and application of learning hierarchical data by embedding it into hyperbolic space. By applying the latest approaches in the field of machine learning and topological data analysis, specifically hyperbolic representations and persistence diagrams we showed the great potential of this method to classify data. We showed good results in classifying MNIST and Hyperspectral images of low resolution which may come in use for civilian and military applications alike.
Our results are comparable to the state-of-the-art DL algorithms in terms of accuracy and most importantly minimization of needed learnable parameters(10-100 fold compared to other algorithms) which result in a quicker learning process and better energy efficiency.

Topological Hyperbolic Representation