Discovery of Empirical Components by Information Theory
Abstract
Recent years have seen exciting new developments in mathematics and computer science, which have opened up new domains of application for computational mathematics. These come with new challenges, for which new approaches and tools must be and are being developed. Machine learning and compressive sensing are two typical examples; they draw not only from traditional linear algebra based numerical analysis or approximation theory, but also from information theory, graph theory, the geometry of Banach spaces, probability theory, and more. This proposal seeks to fund the research of three faculty drawn to these new computational challenges, who are also finding increasingly that their different fields of expertise all contribute to the development of dramatically more effective tools. This confluence of interests, and the conviction that joining their efforts will produce a whole that exceeds the sum of its parts, constitute the engine that drives the approaches proposed here.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 10, 2016
- Accession Number
- AD1013503
Entities
People
- Amit Singer
Organizations
- Princeton University