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Unofficial implementation of SVMs multi-class loss feedback based discriminative dictionary learning in python

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SMLFDL

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 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

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Unofficial implementation of SVMs multi-class loss feedback based discriminative dictionary learning in python

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