Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection

Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are jointly performed. Specifically, the proposed JELSR joins embedding learning with sparse regression to perform feature selection. To show the effectiveness of the proposed framework, we also provide a method using the weight via local linear approximation and adding the l2,1 -norm regularization, and design an effective…

• NN
  National Natural Science Foundation of China

  Awards: 61072093, 61005003, 91120302, 60975038, 61125106
• NK
  National Key Research and Development Program of China

  Award: 2012CB316400