Adaptive Sparse and Low-Rank Models for Real-World Visual Recognition
Abstract
When data is partially-labeled, heterogeneous and imbalanced, the performance of existing methods for visual data analysis can be much worse than expected. The objective of this project is to overcome the fundamental limitations of the current sparse and low-rank modeling techniques to create a new class of methods and algorithms for visual data analysis that will significantly improve the performance of data analysis. Proposed research will focus on the following specific tasks: (1) create sparse and low-rank modeling techniques for imbalanced data; (2) establish theory and algorithms with performance guarantees for learning sparse and low-rank models for multi-modal heterogeneous data; (3) establish theory and algorithms for learning sparse and low-rank models with partially-labeled data; and (4) create efficient scalable sparse and low-rank modeling techniques for large-scale problems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 12, 2017
- Source ID
- W911NF1610126
Entities
People
- Vishal Patel
Organizations
- Army Contracting Command
- Rutgers University
- United States Army