2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-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
SessionMLSP-15: Learning Algorithms 2
LocationGather.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
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.