Geometry-preserving Embeddings: Dimensionality Reduction Techniques for Information Representation

Summary: Recent developments in compressed sensing, machine learning and dimensionality reduction have reinvigorated interest in the theory and applications of embeddings. Embeddings are transformations of signals and sets of signals that approximately preserve some aspects of the geometry of the set, while reducing the complexity of handling such signals.