Dimensionality-Reduction Using Connectionist Networks

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6459

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DC Field	Value	Language
dc.creator	Saund, Eric	-
dc.date	2004-10-04T14:57:08Z	-
dc.date	2004-10-04T14:57:08Z	-
dc.date	1987-01-01	-
dc.date.accessioned	2013-10-09T02:45:33Z	-
dc.date.available	2013-10-09T02:45:33Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-941	-
dc.identifier	http://hdl.handle.net/1721.1/6459	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This paper presents a method for using the self-organizing properties of connectionist networks of simple computing elements to discover a particular type of constraint in multidimensional data. The method performs dimensionality-reduction in a wide class of situations for which an assumption of linearity need not be made about the underlying constraint surface. We present a scheme for representing the values of continuous (scalar) variables in subsets of units. The backpropagation weight updating method for training connectionist networks is extended by the use of auxiliary pressure in order to coax hidden units into the prescribed representation for scalar-valued variables.	-
dc.format	2964058 bytes	-
dc.format	1167730 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-941	-
dc.title	Dimensionality-Reduction Using Connectionist Networks	-
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