Small World Structure Inspired Many to Many Kernel Associative Memory Models and Their Application

Kernel method is an effective and popular trick in machine learning, and small world network is a common phenomenon which exists widely in social fields. In this paper, by introducing them into Hattori et al's multi-module associative memory for many-to-many associations ((MMA) 2 ), a unified framework of small world structure inspired many-to-many kernel associative memory models (SWSI-M 2 KAMs) is proposed. The SWSI-M 2 KAMs not only can store patterns online without more iteration steps, but also extend the range of the processed intelligent information. More importantly, the SWSI-M 2 KAMs framework can develop more new many-to-many associative memory models by selecting different kernel functions and reduce models' configuration complexity by using the sparse small world architecture. Finally, computer simulations demonstrate that the constructed models have good performance on many-to-many associative memory.

[1]  Dao-Qiang Zhang,et al.  Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm , 2003, Neural Processing Letters.

[2]  Laurence B. Milstein,et al.  Spread-Spectrum Communications , 1983 .

[3]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[4]  Zhi-Hua Zhou,et al.  A unified SWSI-KAMs framework and performance evaluation on face recognition , 2005, Neurocomputing.

[5]  Adam Blum,et al.  Bidirectional associative memory systems in C , 1990 .

[6]  Yuzo Hirai,et al.  Mutually linked HASPs: A solution for constraint-satisfaction problems by associative processing , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[8]  J. Mercer.  Functions of positive and negative type, and their connection with the theory of integral equations , 1909 .

[9]  Masafumi Hagiwara,et al.  Multimodule associative memory for many-to-many associations , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[11]  William S. Yamamoto,et al.  AY's Neuroanatomy of C. elegans for Computation , 1992 .

[12]  Masao Nakagawa,et al.  Improved multidirectional associative memories for training sets including common terms , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[13]  Laurence B. Milstein,et al.  Spread Spectrum Communications , 1983, Encyclopedia of Wireless and Mobile Communications.

[14]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[15]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[16]  Masafumi Hagiwara,et al.  Associative memory for intelligent control , 2000 .

[17]  Yuichiro Anzai,et al.  Multimodule Neural Network for Associative Memory , 1992 .