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An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters

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3 Author(s)
Weifeng Liu ; Forecasting Team, Amazon.com, Seattle, WA, USA ; Il Park ; Principe, J.C.

This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.

Published in:

Neural Networks, IEEE Transactions on  (Volume:20 ,  Issue: 12 )