Tensor methods for large-scale learning
Abstract
Learning hidden structures in n-ary relationships inherent in massive amounts of data that are typically generated could lead to state space explosion if handled in a straight forward way. For this STIR work, the PI will address questions of theoretical guarantees that can be obtained with tensors as intentional representation of the data sets. The PI will investigate how to build intentional tensor representations of the n-ary relationships with the proviso that the data structure is built only if needed. The goal is to then construct lower dimensional subspace, where the actual data resides, which then can be used for Machine Learning applications. The approach that the PI will take in showing efficacy of the proposed tensor representation is to show that the errors can be bounded. The PI will also start implementation of the tensor representations to support her theoretical guarantees.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 12, 2017
- Source ID
- W911NF1610134
Entities
People
- Anima Anandkumar
Organizations
- Army Contracting Command
- United States Army
- University of California, Irvine