Training-Efficient Feature Map for Shift-Invariant Kernels

Chen, Xixian; Yang, Haiqin; King, Irwin; Lyu, Michael R.

Training-Efficient Feature Map for Shift-Invariant Kernels

Xixian Chen, Haiqin Yang, Irwin King, Michael R. Lyu

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3395-3400.

Abstract

Random feature map is popularly used to scale up kernel methods. However, employing a large number of mapped features to ensure an accurate approximation will still make the training time consuming. In this paper, we aim to improve the training efficiency of shift-invariant kernels by using fewer informative features without sacrificing precision. We propose a novel feature map method by extending Random Kitchen Sinks through fast data-dependent subspace embedding to generate the desired features. More specifically, we describe two algorithms with different tradeoffs on the running speed and accuracy, and prove that O(l) features induced by them are able to perform as accurately as O(l2) features by other feature map methods. In addition, several experiments are conducted on the real-world datasets demonstrating the superiority of our proposed algorithms.

Xixian Chen, Haiqin Yang, Irwin King, and Michael R. Lyu. 2015. Training-Efficient Feature Map for Shift-Invariant Kernels. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 3395–3400. https://ijcai.org/proceedings/2015/478

@inproceedings{ijcai2015p478,
  title     = {Training-Efficient Feature Map for Shift-Invariant Kernels},
  author    = {Chen, Xixian and Yang, Haiqin and King, Irwin and Lyu, Michael R.},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-15}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Qiang Yang and Michael Wooldridge},
  pages     = {3395--3400},
  year      = {2015},
  month     = {7},
  note      = {Special Track on Machine Learning},
  url       = {https://ijcai.org/proceedings/2015/478},
}

TY  - CPAPER
TI  - Training-Efficient Feature Map for Shift-Invariant Kernels
AU  - Chen, Xixian
AU  - Yang, Haiqin
AU  - King, Irwin
AU  - Lyu, Michael R.
T2  - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2015
SP  - 3395
EP  - 3400
UR  - https://ijcai.org/proceedings/2015/478
ER  -