Dimensionality Reduction in Big Data with Nonnegative Matrix Factorization
Abstract
The grantee shall investigate new methods of dimensionality reduction based on Non-negative Matrix Factorization. This project has two goals: 1) extend simplicial NMF and newly formulate Generative NMF, both with concise interpretability and high quality, 2) design fast and fully parallel distributed algorithms for these two rich NMF models.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 23, 2016
- Source ID
- FA23861514006
Entities
People
- Tu-bao Ho
Organizations
- Air Force Office of Scientific Research
- Japan Advanced Institute of Science and Technology
- United States Air Force