An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters

@article{Liu2009AnIT,
  title={An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters},
  author={Weifeng Liu and Il Memming Park and J. Pr{\'i}ncipe},
  journal={IEEE Transactions on Neural Networks},
  year={2009},
  volume={20},
  pages={1950-1961}
}
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… Expand
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