Convex Principal Feature Selection

  title={Convex Principal Feature Selection},
  author={Mahdokht Masaeli and Yan Yan and Ying Cui and Glenn Fung and Jennifer G. Dy},
A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the original features are important. There is a recent interest in sparse PCA (SPCA). By applying an L1 regularizer to PCA, a sparse transformation is achieved. However, true feature selection may not be achieved as non-sparse coefficients may be distributed over several features. Feature selection is an NP-hard… CONTINUE READING
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