Online Feature Selection with Streaming Features

@article{Wu2013OnlineFS,
  title={Online Feature Selection with Streaming Features},
  author={Xindong Wu and Kui Yu and Wei Ding and Hao Wang and Xingquan Zhu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2013},
  volume={35},
  pages={1178-1192}
}
  • Xindong Wu, Kui Yu, +2 authors Xingquan Zhu
  • Published in
    IEEE Transactions on Pattern…
    2013
  • Computer Science, Medicine
  • We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 40 REFERENCES

    Streaming Feature Selection using IIC

    VIEW 11 EXCERPTS
    HIGHLY INFLUENTIAL

    One-class learning and concept summarization for data streams

    VIEW 1 EXCERPT

    Quadratic Programming Feature Selection

    VIEW 2 EXCERPTS