Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets

Saikat Basu; Manohar Karki; Sangram Ganguly; Robert DiBiano; Supratik Mukhopadhyay; Ramakrishna Nemani

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arXiv · 1509.03413

Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets

Saikat Basu·Manohar Karki·Sangram Ganguly·Robert DiBiano·Supratik Mukhopadhyay·Ramakrishna Nemani

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Abstract

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. 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 and n-MNIST datasets, our framework shows promising results and significantly outperforms traditional Deep Belief Networks.

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Saikat Basu, Manohar Karki, Sangram Ganguly, Robert DiBiano, Supratik Mukhopadhyay, Ramakrishna Nemani. 2015-09-11. Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets. https://arxiv.org/abs/1509.03413

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