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Non-negative Kernel Sparse Coding algorithm for semantic dictionary learning in feature space.
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KSVD_init.mat
NNKSC_main.m
README.md
read_me.txt

README.md

None-Negative Kernel Sparse Coding (NNKSC)

NNKSC is a kernel-based sparse coding and dictionary learning algorithm which enforces non-negativity constraints on the dictionary and the sparse codes. As a result, the learned dictionary atoms and the sparse encodings are more interpretable regarding the semantic characteristics.

Using NNKSC

  • The NNKSC_main.m contain a test run of the NNKSC algorithm on 3 databases.
  • In order to use NNKSC in the supervised setting, (LC-NNKSC) the parameter LC_betta should be tuned to a non-zero weight.
  • More instruction is provided in the read_me.txt file.

The paper

Hosseini, Babak, et al. "Non-negative kernel sparse coding for the analysis of motion data.", International Conference on Artificial Neural Networks (ICANN), Springer, Cham, 2016.

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