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Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets


Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST by adding noise to the MNIST dataset, and three labeled datasets formed by adding noise to the offline Bangla numeral database. Then we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST, n-MNIST and noisy Bangla datasets, our framework shows promising results and outperforms traditional Deep Belief Networks.

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    Our test error on MNIST is different from that in [17] because the authors use a different architecture. This is true both for their Neural Network and DBN architectures.


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The project is supported by Army Research Office (ARO) under Grant #W911-NF1010495 and NASA Carbon Monitoring System through Grant #NNH14ZD-A001NCMS. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the ARO or the United States Government. We are thankful to the Computer Vision and Pattern Recognition unit at Indian Statistical Institute, Kolkata, India for making the offline Bangla handwritten numeral dataset available to us.

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Correspondence to Saikat Basu.

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This is an extended version of the paper published in ESANN 2015, [1].

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Basu, S., Karki, M., Ganguly, S. et al. Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets. Neural Process Lett 45, 855–867 (2017).

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  • Deep neural networks
  • Handwritten digit classification
  • Probabilistic quadtrees
  • Deep belief networks
  • Sparse feature representation