Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets

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 quad trees and Deep Belief Nets. On the MNIST and n-MNIST data sets, our framework shows promising results and significantly outperforms traditional Deep Belief Networks.

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Document Details

Document Type
Technical Report
Publication Date
Apr 24, 2015
Accession Number
AD1010904

Entities

People

  • Manohar Karki
  • Ramakrishna Nemani
  • Robert Di Biano
  • Saikat Basu
  • Sangram Ganguly
  • Supratik Mukhopadhyay

Organizations

  • Louisiana State University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Automated Speech Recognition
  • Computer Science
  • Deep Belief Networks
  • Deep Learning
  • Gaussian Noise
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks