Uncertainty and Feature Learning with Kernel Machines for ATR
Abstract
In this grant, we apply a recently developed uncertainty principle inspired by information theoretic learning to applications of interest to the Navy. Our uncertainty measure is non-intrusive to the training process of point-wise prediction models, and has the great advantage of being much faster to compute than traditional techniques. Hence, it can be potentially used for online applicationsin computational restricted environments. We propose to improve the man-machine interface for machine learning operators using uncertainty with the goal of improving the acceptability and trust of ML to effectively boost performance. Another important application is to carefully select exemplars to be added to ATR training sets to improve the performance of ML algorithms to save time and money when creating the databases for the Navy missions.Furthermore, we will also apply a recently developed close form solution to nonlinear prediction problems that will avoid the need for the huge amounts of data required to train conventional machine learning models. We called this novel algorithm the Functional Wiener Filter (FWF). In testing mode, the complexity is O(K2) where K is the order of the filter, and can be easily implemented in ultra-low power hardware. With these properties, we can automatically configure classifiers to any specific scenario. This is a major achievement andwe plan to extend it for images such that can quantify sea floor texture (spatial statistics) that can be used as contextual information for ATR to avoid many of the false alarms. Better understanding of the RKHS for classification, preserving explainability, and reducing computation w.r.t. deep learning will also be crucial for ATR and many other applications ofautonomy.Approved for Public Release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 10, 2025
- Source ID
- N000142512223
Entities
People
- José PrÃncipe
Organizations
- Office of Naval Research
- United States Navy
- University of Florida