https://github.com/realamirhe/smlfdl

Unofficial implementation of SVMs multi-class loss feedback based discriminative dictionary learning in python
https://github.com/realamirhe/smlfdl

algorithm auto-encoder classification cybernetics feedback-mechanism ksvd multi-svm smlfdl

Last synced: about 2 months ago
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Unofficial implementation of SVMs multi-class loss feedback based discriminative dictionary learning in python

• Host: GitHub
• URL: https://github.com/realamirhe/smlfdl
• Owner: realamirhe
• License: mit
• Created: 2021-07-04T19:42:52.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-04-13T04:05:22.000Z (about 3 years ago)
• Last Synced: 2025-02-13T21:16:33.634Z (4 months ago)
• Topics: algorithm, auto-encoder, classification, cybernetics, feedback-mechanism, ksvd, multi-svm, smlfdl
• Language: Python
• Homepage:
• Size: 783 KB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
![SMLFDL](https://socialify.git.ci/amirHossein-Ebrahimi/SMLFDL/image?description=1&descriptionEditable=SVMs%20multi-class%20loss%20feedback%20based%20discriminative%20dictionary%20learning&font=Inter&language=1&pattern=Plus&theme=Light)

## 📄[article][SMLFDL_article]

SVMs multi-class loss feedback based discriminative dictionary learning for image classification

> SMLFDL integrates dictionary learning and support vector machines training into a unified learning

framework by looping the designed multi-class loss term, which

is inspired by the feedback mechanism in cybernetics.

analysis has been done on scene-15 dataset.   

Feature vectors has been prepared by four-level `spatial pyramid`, dense `DAISY` feature description followed by PCA.  

As article proposed SMLFDL are faster in predictions and converge in lower epochs.  

code for features will be added soon.

## Highlights:

- Inspired by the feedback mechanism in cybernetics, a novel discriminative dictionary learning framework, named support vector machines (SVMs) multi-class loss feedback based discriminative dictionary learning (SMLFDL) is proposed to learn a dictionary while training SVMs. As far as we know, it is the first time that the feedback mechanism in cybernetics is adopted for constructing dictionary learning model.

- SMLFDL further employ the Fisher discrimination criterion on the coding coefficients under -norm constraint to make the coding coefficients have small intra-class scatter but big inter-class scatter for countering intra-class variability of datasets.

- An efficient and practical SMLFDL optimization algorithm is presented to learn a dictionary while training SVMs. Experimental results on several widely used image databases show that SMLFDL can achieve a competitive performance with other state-of-the-art methods on classification task.

## Notes: 

**The original article was developed in matlab**

The [report file](https://github.com/realamirhe/SMLFDL/blob/master/SMLFDL%20report.pdf) is an over-view showing precedures and some figures and didn't published anywhere, it must not be refernece any where, for refernece use [original article][SMLFDL_article]

[SMLFDL_article]: https://www.sciencedirect.com/science/article/abs/pii/S0031320320304933