# Label Embedded Dictionary Learning for Image Classification

arXiv: https://arxiv.org/abs/1903.03087 · HTML: https://mlsanity.com/paper/1903.03087/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [MHFC: Multi-Head Feature Collaboration for Few-Shot Learning](https://mlsanity.com/paper/2109.07785/) (2021)
- [MDFM: Multi-Decision Fusing Model for Few-Shot Learning](https://mlsanity.com/paper/2112.00690/) (2021)
- [GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning](https://mlsanity.com/paper/2203.07738/) (2022)
- [A Unified Probabilistic Framework for Dictionary Learning with Parsimonious Activation](https://mlsanity.com/paper/2509.25690/) (2025)
- [Carton dataset synthesis method for domain shift based on foreground texture decoupling and replacement](https://mlsanity.com/paper/2103.10738/) (2021)
- [CIM: Class-Irrelevant Mapping for Few-Shot Classification](https://mlsanity.com/paper/2109.02840/) (2021)
- [SSDL: Self-Supervised Dictionary Learning](https://mlsanity.com/paper/2112.01790/) (2021)
- [Driving behavior recognition via self-discovery learning](https://mlsanity.com/paper/2503.14194/) (2025)

## Abstract (arXiv)

Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully applied in image classification. The objective function of LC-KSVD is consisted of reconstruction error, classification error and discriminative sparse codes error with L0-norm sparse regularization term. The L0-norm, however, leads to NP-hard problem. Despite some methods such as orthogonal matching pursuit can help solve this problem to some extent, it is quite difficult to find the optimum sparse solution. To overcome this limitation, we propose a label embedded dictionary learning (LEDL) method to utilise the L1-norm as the sparse regularization term so that we can avoid the hard-to-optimize problem by solving the convex optimization problem. Alternating direction method of multipliers and blockwise coordinate descent algorithm are then exploited to optimize the corresponding objective function. Extensive experimental results on six benchmark datasets illustrate that the proposed algorithm has achieved superior performance compared to some conventional classification algorithms.