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Author (up) Adriana Romero; Petia Radeva; Carlo Gatta edit   pdf
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  Title No more meta-parameter tuning in unsupervised sparse feature learning Type Miscellaneous
  Year 2014 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.
 
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  Notes MILAB; LAMP; 600.079 Approved no  
  Call Number Admin @ si @ RRG2014 Serial 2471  
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