Robust Learning by Self-organization of Nonlinear Lines of Attractions
A mathematical model for learning a nonlinear line of attractions is presented in this paper. This model encapsulates attractive fixed points scattered in the state space representing patterns with similar characteristics as an attractive line. The dynamics of this nonlinear line attractor network is designed to operate between stable and unstable states. These criteria can be used to circumvent the plasticity-stability dilemma by using the unstable state as an indicator to create a new line for an unfamiliar pattern. This novel learning strategy utilized stability (convergence) and instability (divergence) criteria of the designed dynamics to induce self-organizing behavior. The self-organizing behavior of the nonlinear line attractor model can helps to create complex dynamics in an unsupervised manner. Experiments performed on CMU face expression database shows that the proposed model can perform pattern association and pattern classification tasks with few iterations and great accuracy.
KeywordsFace Image Instability Mode Associative Memory Recurrent Network Unsupervised Manner
Unable to display preview. Download preview PDF.
- 1.Seung, H.S.: Learning Continuous Attractors in Recurrent Networks. Advances in Neural Information Processing Systems, 654–660 (1998)Google Scholar
- 3.Freeman, W.J., Barrie, J.M.: Analysis of Spatial Patterns of Phase in Neocortical Gamma EEGs in Rabbit. Journal of Neurophysiology 84, 1266–1278 (2000)Google Scholar
- 4.Harter, D., Kozma, R.: Nonconvergent Dynamics and Cognitive Systems. Cognitive Science (2003)Google Scholar
- 9.Liu, X., Chen, T., Vijaya Kumar, B.V.K.: Face Authentication for Multiple Subjects Using Eigenflow. Pattern Recognition: special issue on Biometric 36, 313–328 (2003)Google Scholar