Transport Model for Feature Extraction

TL;DR

This paper introduces a novel feature extraction method based on transport operators on graphs, extending diffusion-based techniques to dynamical systems, with applications in hyperspectral image classification.

Contribution

It proposes a new transport operator framework for feature extraction that captures complex relationships beyond traditional diffusion methods.

Findings

Demonstrates the method's flexibility through diverse transformations.

Shows improved classification performance on hyperspectral satellite imagery.

Provides theoretical properties of the transport operators.

Abstract

We present a new feature extraction method for complex and large datasets, based on the concept of transport operators on graphs. The proposed approach generalizes and extends the many existing data representation methodologies built upon diffusion processes, to a new domain where dynamical systems play a key role. The main advantage of this approach comes from the ability to exploit different relationships than those arising in the context of e.g., Graph Laplacians. Fundamental properties of the transport operators are proved. We demonstrate the flexibility of the method by introducing several diverse examples of transformations. We close the paper with a series of computational experiments and applications to the problem of classification of hyperspectral satellite imagery, to illustrate the practical implications of our algorithm and its ability to quantify new aspects of relationships within complicated datasets.