| Paper ID | MLSP-15.2 |
| Paper Title |
TRAINING A BANK OF WIENER MODELS WITH A NOVEL QUADRATIC MUTUAL INFORMATION COST FUNCTION |
| Authors |
Bo Hu, Jose C. Principe, University of Florida, United States |
| Session | MLSP-15: Learning Algorithms 2 |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-INFO] Information-theoretic learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This paper presents a novel training methodology to adapt parameters of a bank of Wiener models (BWMs), i.e., a bank of linear filters followed by a static memoryless nonlinearity, using full pdf information of the projected outputs and the desired signal. BWMs also share the same architecture with the first layer of a time-delay neural networks (TDNN) with a single hidden layer, which is often trained with backpropagation. To optimize BWMs, we develop a novel cost function called the empirical embedding of quadratic mutual information (E-QMI) that is metric-driven and efficient in characterizing the statistical dependency. We demonstrate experimentally that by applying this cost function to the proposed model, our method is comparable with state-of-the-art neural network architectures for regressions tasks without using backpropagation of the error. |
