K-LDA: An algorithm for learning jointly overcomplete and discriminative dictionaries

K-LDA: An algorithm for learning jointly overcomplete and discriminative dictionaries. Golmohammady, J., Joneidi, M., Sadeghi, M., Babaie-Zadeh, M., & Jutten, C. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 775-779, Sep., 2014.

Paper abstract bibtex

A new algorithm for learning jointly reconstructive and discriminative dictionaries for sparse representation (SR) is presented. While in a usual dictionary learning algorithm like K-SVD only the reconstructive aspect of the sparse representations is considered to learn a dictionary, in our proposed algorithm, which we call K-LDA, the discriminative aspect of the sparse representations is also addressed. In fact, K-LDA is an extension of K-SVD in the case that the class informations (labels) of the training data are also available. K-LDA takes into account these information in order to make the sparse representations more discriminate. It makes a trade-off between the amount of reconstruction error, sparsity, and discrimination of sparse representations. Simulation results on synthetic and hand-written data demonstrate the promising performance of our proposed algorithm.

@InProceedings{6952254,
  author = {J. Golmohammady and M. Joneidi and M. Sadeghi and M. Babaie-Zadeh and C. Jutten},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {K-LDA: An algorithm for learning jointly overcomplete and discriminative dictionaries},
  year = {2014},
  pages = {775-779},
  abstract = {A new algorithm for learning jointly reconstructive and discriminative dictionaries for sparse representation (SR) is presented. While in a usual dictionary learning algorithm like K-SVD only the reconstructive aspect of the sparse representations is considered to learn a dictionary, in our proposed algorithm, which we call K-LDA, the discriminative aspect of the sparse representations is also addressed. In fact, K-LDA is an extension of K-SVD in the case that the class informations (labels) of the training data are also available. K-LDA takes into account these information in order to make the sparse representations more discriminate. It makes a trade-off between the amount of reconstruction error, sparsity, and discrimination of sparse representations. Simulation results on synthetic and hand-written data demonstrate the promising performance of our proposed algorithm.},
  keywords = {signal processing;singular value decomposition;K-LDA;discriminative dictionaries;reconstructive dictionaries;sparse representation;dictionary learning algorithm;K-SVD;Dictionaries;Signal processing algorithms;Training data;Image reconstruction;Training;Vectors;Linear programming;Dictionary Learning;Singular Value Decomposition;Linear Discriminant Analysis;Discriminative Learning},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925501.pdf},
}

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