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Author (up) Adriana Romero; Petia Radeva; Carlo Gatta edit  doi
openurl 
  Title Meta-parameter free unsupervised sparse feature learning Type Journal Article
  Year 2015 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 37 Issue 8 Pages 1716-1722  
  Keywords  
  Abstract 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 CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well.  
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  Area Expedition Conference  
  Notes MILAB; 600.068; 600.079; 601.160 Approved no  
  Call Number Admin @ si @ RRG2014b Serial 2594  
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