(YIP) Sub-Linear Algorithms for Learning and Sensing with Multiple Disparate and Ultra-High-Dimensional Dataset
Abstract
Compressed Sensing via Compute and Memory Efficient Algorithms: Develop novel and rigorous (mathematically provable) hashing algorithms for efficient recovery of the signal from massive and ultra-high-dimensional measurements. The focus will be on sub-linear, both in dimension and the number of samples, hashing algorithms which can significantly improve the algorithmic efficiency of current sparse optimizations and greedy matching pursuit algorithms.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 09, 2018
- Source ID
- FA95501810152
Entities
People
- Anshumali Shrivastava
Organizations
- Air Force Office of Scientific Research
- Rice University
- United States Air Force