# Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets

arXiv: https://arxiv.org/abs/1509.03413 · HTML: https://mlsanity.com/paper/1509.03413/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2015.

## Built on by (most cited first, 9 of 9)

- [Heterogeneous Contrastive Learning](https://mlsanity.com/paper/2105.09401/) (2021)
- [A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems](https://mlsanity.com/paper/2301.07799/) (2023)
- [Boosting the Power of Kernel Two-Sample Tests](https://mlsanity.com/paper/2302.10687/) (2023)
- [Implicit Generative Prior for Bayesian Neural Networks](https://mlsanity.com/paper/2404.18008/) (2024)
- [Robust Unpaired Single Image Super-Resolution of Faces](https://mlsanity.com/paper/2201.09109/) (2022)
- [Non-Linear Spectral Dimensionality Reduction Under Uncertainty](https://mlsanity.com/paper/2202.04678/) (2022)
- [Dirichlet process mixture models for non-stationary data streams](https://mlsanity.com/paper/2210.06872/) (2022)
- [Noise robust neural network architecture](https://mlsanity.com/paper/2305.09276/) (2023)
- [Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks](https://mlsanity.com/paper/2604.11833/) (2026)

## Abstract (arXiv)

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.