Johnson, M. and Brown, M. and Allinson, N. M. (1990) Multidimensional self organisation. In: Cellular Neural Networks and their Applications, 1990. CNNA90, 1619 December 1990, Budapest.
Full content URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...
Documents 

PDF
00207530.pdf  Whole Document Restricted to Repository staff only 596kB 
Item Type:  Conference or Workshop contribution (Paper) 

Item Status:  Live Archive 
Abstract
Presents a technique that may be used for clustering in a very high dimensionality pattern space. The desirability of a self organising algorithm which can learn an internal representation for use in a pattern recogniser is shown. Using such an algorithm, subspace methods are brought together with an associative memory to form a pattern recogniser which employs unsupervised learning. The representation used for signal pattern clusters is based on topologically ordered units, each of which can label a complex area of pattern space. An adaption algorithm is given and shown to be insensitive to the variation in vector magnitudes which is found within a typical training set. A number of examples are given showing clustering of real grey scale, visual data and the reconstruction of exemplars using adaptive feedback. The application of this to vector quantisation and noise removal is demonstrated
Additional Information:  Presents a technique that may be used for clustering in a very high dimensionality pattern space. The desirability of a self organising algorithm which can learn an internal representation for use in a pattern recogniser is shown. Using such an algorithm, subspace methods are brought together with an associative memory to form a pattern recogniser which employs unsupervised learning. The representation used for signal pattern clusters is based on topologically ordered units, each of which can label a complex area of pattern space. An adaption algorithm is given and shown to be insensitive to the variation in vector magnitudes which is found within a typical training set. A number of examples are given showing clustering of real grey scale, visual data and the reconstruction of exemplars using adaptive feedback. The application of this to vector quantisation and noise removal is demonstrated 

Keywords:  adaptive systems, learning systems, neural nets, pattern recognition, selfadjusting systems, topology 
Subjects:  G Mathematical and Computer Sciences > G730 Neural Computing 
Divisions:  College of Science > School of Computer Science 
ID Code:  5070 
Deposited On:  20 Apr 2012 12:01 
Repository Staff Only: item control page