An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features
Abstract
:1. Introduction: Reconciling Competition and Cooperation
2. Unsupervised Learning and the Optimal Representation of Images
2.1. Algorithm: Sparse Coding with a Control Mechanism for the Selection of Atoms
Algorithm 1 Generalized Matching Pursuit: ${\mathbf{a}}_{k}=S({\mathbf{y}}_{k};\Psi =\{\Phi ,z,{N}_{0}\})$ 

2.2. Algorithm: Histogram Equalization Homeostasis
2.3. Results: A More Efficient Unsupervised Learning Using Homeostasis
Algorithm 2 Homeostatic Unsupervised Learning of Kernels: $\Phi =H(\mathbf{y};\eta ,{\eta}_{h},{N}_{0})$ 

3. Discussion and Conclusions
Supplementary Materials
Funding
Acknowledgments
Conflicts of Interest
References
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Perrinet, L.U. An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features. Vision 2019, 3, 47. https://doi.org/10.3390/vision3030047
Perrinet LU. An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features. Vision. 2019; 3(3):47. https://doi.org/10.3390/vision3030047
Chicago/Turabian StylePerrinet, Laurent U. 2019. "An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features" Vision 3, no. 3: 47. https://doi.org/10.3390/vision3030047