1 research outputs found
Transport Model for Feature Extraction
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