# Deep Dictionary Learning with An Intra-class Constraint

arXiv: https://arxiv.org/abs/2207.06841 · HTML: https://mlsanity.com/paper/2207.06841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition with Limited Data](https://mlsanity.com/paper/2005.10940/) (2020)
- [Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering](https://mlsanity.com/paper/2005.01317/) (2020)
- [Object Classification with Joint Projection and Low-rank Dictionary Learning](https://mlsanity.com/paper/1612.01594/) (2016)
- [Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2111.13920/) (2021)

## Abstract (arXiv)

In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for representation learning and visual recognition.~However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information.~To make full use of the category information of different samples, we propose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experimental results on four image datasets show that our method is superior to the state-of-the-art methods.