Learning sparse feature representations using probabilistic quadtrees and Deep Belief Nets
Document Type
Conference Proceeding
Publication Date
1-1-2015
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.
Publication Source (Journal or Book title)
23rd European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning Esann 2015 Proceedings
First Page
367
Last Page
372
Recommended Citation
Basu, S., Karki, M., Ganguly, S., DiBiano, R., Mukhopadhyay, S., & Nemani, R. (2015). Learning sparse feature representations using probabilistic quadtrees and Deep Belief Nets. 23rd European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning Esann 2015 Proceedings, 367-372. Retrieved from https://repository.lsu.edu/enviro_sciences_pubs/422