Corpus ID: 26687912

Multilinear Regression for Embedded Feature Selection with Application to fMRI Analysis

@inproceedings{Song2017MultilinearRF,
  title={Multilinear Regression for Embedded Feature Selection with Application to fMRI Analysis},
  author={Xiaonan Song and Haiping Lu},
  booktitle={AAAI},
  year={2017}
}
  • Xiaonan Song, Haiping Lu
  • Published in AAAI 2017
  • Computer Science
  • Embedded feature selection is effective when both prediction and interpretation are needed. The Lasso and its extensions are standard methods for selecting a subset of features while optimizing a prediction function. In this paper, we are interested in embedded feature selection for multidimensional data, wherein (1) there is no need to reshape the multidimensional data into vectors and (2) structural information from multiple dimensions are taken into account. Our main contribution is… CONTINUE READING

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