# Greedy Deep Dictionary Learning

arXiv: https://arxiv.org/abs/1602.00203 · HTML: https://mlsanity.com/paper/1602.00203/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2016.

## Built on by (most cited first, 3 of 3)

- [A Unified Probabilistic Framework for Dictionary Learning with Parsimonious Activation](https://mlsanity.com/paper/2509.25690/) (2025)
- [The Finite Element Neural Network Method: One Dimensional Study](https://mlsanity.com/paper/2501.12508/) (2025)
- [On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning](https://mlsanity.com/paper/2606.22352/) (2026)

## Abstract (arXiv)

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning tools like discriminative KSVD and label consistent KSVD. Our method yields better results than all.