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📄[article][SMLFDL_article]\nSVMs multi-class loss feedback based discriminative dictionary learning for image classification\n\n\u003e SMLFDL integrates dictionary learning and support vector machines training into a unified learning\nframework by looping the designed multi-class loss term, which\nis inspired by the feedback mechanism in cybernetics.\n\nanalysis has been done on scene-15 dataset.   \nFeature vectors has been prepared by four-level `spatial pyramid`, dense `DAISY` feature description followed by PCA.  \nAs article proposed SMLFDL are faster in predictions and converge in lower epochs.  \n\u003csub\u003ecode for features will be added soon.\u003c/sub\u003e\n\n\n## Highlights:\n- 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.\n\n- 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.\n\n- 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.\n\n## Notes: \n**The original article was developed in matlab**\n\nThe [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]\n\n\n\n[SMLFDL_article]: https://www.sciencedirect.com/science/article/abs/pii/S0031320320304933\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frealamirhe%2Fsmlfdl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frealamirhe%2Fsmlfdl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frealamirhe%2Fsmlfdl/lists"}