说明：  We propose a novel framework for thedeterministicconstruction oflinear, near-isometric embeddings of a nite set of data points.
(We demonstrate that our framework is useful for a number of applications in machine learning and signal processing via a range of experiments on large-scale synthetic and real datasets.)