A Cognitive Data Visualization Method Based on Hyper Surface

The understanding of data is highly relevant to how one senses and perceives them. The existing approaches for classification have been developed mainly based on exploring the intrinsic structure of dataset itself less or no emphasis paid on simulating human visual cognition. A new hyper surface classification method (HSC) has been studied since 2002. HSC is a universal classification method, in which a model of hyper surface is obtained by adoptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan curve theorem in topology. In this paper we point out that HSC is a cognitive data visualization method. Simulation results show the effectiveness of the proposed method on large test data with complex distribution and high density. In particular, we show that HSC can very often bring a significant reduction of computation effort without loss of prediction capability.

[1]  Daewon Lee,et al.  Equilibrium-Based Support Vector Machine for Semisupervised Classification , 2007, IEEE Transactions on Neural Networks.

[2]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[3]  Ron Kohavi,et al.  The Wrapper Approach , 1998 .

[4]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[5]  Zhongzhi Shi,et al.  A novel classification method based on hypersurface , 2003 .

[6]  D. Hubel.  Eye, brain, and vision , 1988 .

[7]  John R. Harper,et al.  Algebraic topology : a first course , 1982 .

[8]  Hao Feng,et al.  The application of DBF neural networks for object recognition , 2004, Inf. Sci..

[9]  S. Coren,et al.  In Sensation and perception , 1979 .

[10]  Wang Shou-jue.  Bionic (Topological) Pattern Recognition——A New Model of Pattern Recognition Theory and Its Applications , 2002 .

[11]  Deyu Meng,et al.  A New Approach for Classification: Visual Simulation Point of View , 2005, ISNN.

[12]  William Fulton.  Algebraic Topology: A First Course , 1995 .

[13]  Max A. Viergever,et al.  Scale-Space Theory in Computer Vision , 1997 .

[14]  Qing He,et al.  The classification method based on hyper surface , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[15]  Qing He,et al.  HyperSurface Classifiers Ensemble for High Dimensional Data Sets , 2006, ISNN.

[16]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[17]  Bo Zhang,et al.  A geometrical representation of McCulloch-Pitts neural model and its applications , 1999, IEEE Trans. Neural Networks.