Lizard Learning Algorithm for Invariant Pattern Recognition
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Copyright: © 2020 G. M. Rao, G. R. Babu and G. V. Kumari. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Researches are keen to know astonishing and intricate details of the nature. Each creature has its own admiring abilities and performs their routine task in more efficient manner. The bug navigation system has drawn keen attention among research community to know how they are able to perform their routine task in utmost skillful manner. The lizard is capable of identifying slowly varying features and able to trap the insects with more admiring skill set. The Lizard Learning Algorithm (LLA) was proposed for tracking invariant features which uses modified slow feature analysis. The article covers mathematical treatment for the slow feature analysis, proposed modification, higher order neural network training and ORL database for experimentation purpose. The results are most pleasing compared to conventional classifiers for the invariant features.
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- Slow feature analysis
- higher order neural network
- eye crapping
- unsupervised feature extraction
- invariant pattern recognition