{"title":"A Brain Inspired Approach for Multi-View Patterns Identification","authors":"Yee Ling Boo, Damminda Alahakoon","volume":47,"journal":"International Journal of Computer and Information Engineering","pagesStart":1746,"pagesEnd":1756,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/11268","abstract":"
Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.<\/p>\r\n","references":"[1] C. B. Saper, S. Iversen, and R. Frackowiak, \"Integration of sensory\r\nand motor function: The association areas of the cerebral cortex and\r\nthe cognitive capabilities of the brain,\" in Principles of neural science,\r\n4th ed., E. R. Kandel, J. H. Schwartz, and T. M. Jessell, Eds. New\r\nYork: McGraw-Hill, 2000, ch. 19, pp. 349-380.\r\n[2] V. Mountcastle, \"The columnar organization of the neocortex.\" Brain,\r\nno. 120, pp. pp. 701-722, 1997.\r\n[3] K. Friston, \"Hierarchical models in the brain,\" PLoS Computational\r\nBiology, vol. 4, no. 11, 2008.\r\n[4] D. L. Hall and J. Llinas, \"An introduction to multisensor data fusion,\"\r\nProceedings of the IEEE, vol. 85, no. 1, pp. pp. 6-23, 1997.\r\n[5] J. R. Hobbs, \"Granularity,\" in Proceedings of the 9th International Joint\r\nConference on Artificial Intelligence (IJCAI). Los Angeles, USA:\r\nMorgan Kaufmann, 1985, pp. 432-435.\r\n[6] Y. Yao, \"Perspectives of granular computing,\" in IEEE International\r\nConference on Granular Computing (GrC), Beijing, China, 2005, pp.\r\n85-90.\r\n[7] Y. Chen and Y. Yao, \"A multiview approach for intelligent data analysis\r\nbased on data operators,\" International Journal of Information Sciences,\r\nvol. 178, pp. pp. 1-20, 2008.\r\n[8] \u00d4\u00c7\u00f6\u00d4\u00c7\u00f6, \"Multiview intelligent data analysis based on granular computing,\"\r\nin IEEE International Conference on Granular Computing (GrC),\r\nAtlanta, USA, 2006, pp. 281-286.\r\n[9] M. Minsky, The Emotion Machine : commensense thinking, artificial\r\nintelligence, and the future of the human mind. New York: Simon &\r\nSchuster, 2006.\r\n[10] S. Zhang, C. Zhang, and X. Wu, Knowledge Discovery in Multiple\r\nDatabases. London, UK: Springer-Verlag, 2004.\r\n[11] N. Kasabov, E. Postma, and J. van den Herik, \"Avis: a connectionistbased\r\nframework for integrated auditory and visual information processing,\"\r\nInformation Sciences, vol. 123, pp. pp. 127-148, 2000.\r\n[12] N. Kasabov, \"Evolving systems for integrated multi-modal information\r\nprocessing,\" in Evolving Connectionist Systems: Methods and Applications\r\nin Bioinformatics, Brain Study and Intelligent Machine. London:\r\nSpringer-Verlag, 2003, ch. 13, pp. 257-271.\r\n[13] \u00d4\u00c7\u00f6\u00d4\u00c7\u00f6, \"Evolving intelligent systems for adaptive multimodal information\r\nprocessing,\" in Evolving Connectionist Systems The Knowledge Engineering\r\nApproach. London: Springer-Verlag, 2007, ch. 13, pp. 361-\r\n380.\r\n[14] J. Hawkins and S. Blakeslee, On Intelligence. New York: Times Books,\r\n2004.\r\n[15] J. Hawkins and D. George, \"Hierarchical temporal memory - concepts,\r\ntheory, and terminology,\" Numenta Inc., Redwood City, California,\r\nWhite Paper, 2007.\r\n[16] Y. Lu, \"Concept hierarchy in data mining: Specification,generation and\r\nimplementation,\" Master-s thesis, Simon Fraser University, Canada,\r\n1997.\r\n[17] D. Alahakoon, S. K. Halgamuge, and B. Srinivasan, \"A self growing\r\ncluster development approach to data mining,\" in IEEE Conference\r\nSystems, Man and Cybernetics, San Diego, USA, 1998, pp. 2901-2906.\r\n[18] \u00d4\u00c7\u00f6\u00d4\u00c7\u00f6, \"Dynamic self-organizing maps with controlled growth for knowledge\r\ndiscovery,\" IEEE Transactions on Neural Networks, vol. 11, no. 3,\r\npp. pp. 601-614, 2000.\r\n[19] R. S. Forsyth, \"Uci machine learning repository, zoo data set,\" 1990.\r\n(Online). Available: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Zoo","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 47, 2010"}