Representative Selection with Structured Sparsity

Hongxing Wang, Yoshinobu Kawahara, Chaoqun Weng, Junsong Yuan

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


We propose a novel formulation to find representatives in data samples via learning with structured sparsity. To find representatives with both diversity and representativeness, we formulate the problem as a structurally-regularized learning where the objective function consists of a reconstruction error and three structured regularizers: (1) group sparsity regularizer, (2) diversity regularizer, and (3) locality-sensitivity regularizer. For the optimization of the objective, we propose an accelerated proximal gradient algorithm, combined with the proximal-Dykstra method and the calculation of parametric maximum flows. Experiments on image and video data validate the effectiveness of our method in finding exemplars with diversity and representativeness and demonstrate its robustness to outliers.

Original languageEnglish
Pages (from-to)268-278
Number of pages11
JournalPattern Recognition
Publication statusPublished - Mar 1 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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