The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Effect of connectivity in an associative memory model
Journal of Computer and System Sciences
A Bidirectional Associative Memory Based on Optimal Linear Associative Memory
IEEE Transactions on Computers
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Face recognition with one training image per person
Pattern Recognition Letters
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Robustness in neural computation: random graphs and sparsity
IEEE Transactions on Information Theory
Recurrent correlation associative memories
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Multivalued associative memories based on recurrent networks
IEEE Transactions on Neural Networks
Adaptation of the relaxation method for learning in bidirectional associative memory
IEEE Transactions on Neural Networks
Neurocomputing
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The Kernel method is an effective and popular trick in machine learning. In this paper, by introducing it into conventional auto-associative memory models (AMs), we construct a unified framework of kernel auto-associative memory models (KAMs), which makes the existing exponential and polynomial AMs become its special cases. Further, in order to reduce KAM's connect complexity, inspired by ''small-world network'' recently described by Watts and Strogatz, we propose another unified framework of small-world structure (SWS) inspired kernel auto-associative memory models (SWSI-KAMs), which, in principle, makes KAMs simpler in structure. Simulation results on the FERET face database show that, the SWSI-KAMs adopting kernels such as Exponential and Hyperbolic tangent kernels have advantages of configuration simplicity while their recognition performance is almost as good as or even better than corresponding KAMs with full connectivity. In the end, the SWSI-KAM adopting Exponential kernel with different connectivities was emphatically investigated for robustness based on those face images which were added random noises and/or partially occluded in a mosaic way, and the experiments demonstrate that the SWSI-KAM with Exponential kernel is more robust in all cases of network connectivity of 20%, 40% and 60% than both PCA and recently proposed (PC)^2A algorithms for face recognition. cognition.