Fusion-Based Robust Signal Processing by Humans and Machines
Department of Biomedical Engineering, Oregon Health & Science University
Information and Intelligent Systems, National Science Foundation
Although many existing automatic pattern recognition systems have achieved considerable success over the past fifty years, the performance of most of these systems deteriorates drastically in unpredictable and changing environments. In contrast, biological systems seem to be much more resilient to the environmental changes that are not relevant to their recognition tasks. The differences in the performance between machine and biological learning approaches motivated us to explore new methods for pattern recognition based on high-dimensional representation, learning with partial information, and on rapidly adapting information fusion. In the first part of my presentation, I will discuss the notion of robustness and some of the reasons for the deterioration in performance in typical pattern-recognition systems that are based on dimension reduction. I will then illustrate several relevant properties of human perceptual systems. In the third part of the talk I will describe ways that a pattern-recognition system can confront the problem of unpredictable and changing environmental conditions.