High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

@article{Yamada2014HighDimensionalFS,
  title={High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso},
  author={M. Yamada and Wittawat Jitkrittum and L. Sigal and E. Xing and Masashi Sugiyama},
  journal={Neural Computation},
  year={2014},
  volume={26},
  pages={185-207}
}
The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this letter, we consider a feature-wise kernelized Lasso for capturing nonlinear input-output dependency. We first show that with particular choices of kernel functions, nonredundant… Expand
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