A brain inspired approach for multi-view patterns identification

Boo, Yee Ling and Alahakoon, Damminda 2010, A brain inspired approach for multi-view patterns identification, World Academy of science, engineering and technology, vol. 4, no. 11, pp. 719-728.

Attached Files
Name Description MIMEType Size Downloads

Title A brain inspired approach for multi-view patterns identification
Author(s) Boo, Yee Ling
Alahakoon, Damminda
Contributor(s) Ardil, Cemal
Journal name World Academy of science, engineering and technology
Volume number 4
Issue number 11
Start page 719
End page 728
Total pages 10
Publisher WASET
Place of publication [Venice, Italy]
Publication date 2010-11
ISSN 2070-3740
1307-6892
Keyword(s) Multimodal
Granularity
Hierarchical Clustering
Growing Self Organising Maps
Data Mining
Summary Biologically human brain processes information in both uniimodal 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.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Persistent URL http://hdl.handle.net/10536/DRO/DU:30033206

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 453 Abstract Views, 5 File Downloads  -  Detailed Statistics
Created: Thu, 03 Mar 2011, 13:01:24 EST by Katrina Fleming

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.