ARISTOTLE UNIVERSITY OF THESSALONIKI
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20/07/2000, Page 1 HYPERGEO 1 st technical verification ARISTOTLE UNIVERSITY OF THESSALONIKI Word Category Map Creation A. Georgakis, C. Kotropoulos, I. Pitas
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ARISTOTLE UNIVERSITY OF THESSALONIKI. Word Category Map Creation A. Georgakis, C. Kotropoulos, I. Pitas. ARISTOTLE UNIVERSITY OF THESSALONIKI. Dimensionality reduction. - PowerPoint PPT Presentation
Transcript of ARISTOTLE UNIVERSITY OF THESSALONIKI
20/07/2000, Page 1HYPERGEO 1st technical verification
ARISTOTLE UNIVERSITY OF THESSALONIKI
Word Category Map Creation
A. Georgakis, C. Kotropoulos, I. Pitas
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Due to the size of the feature vectors two techniques were applied in order to reduce the dimensionality of the input space, to increase the overall performance and the speed:
1.1.Random projectionRandom projection:a small fraction of each vector’s co-ordinates is kept for further processing.
Dimensionality reductionDimensionality reduction
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•Fast winner searchFast winner search:from the remaining co-ordinates those with the highest variance were used to determine the “winning” node.
Dimension of the original space: 512Dimension of the sub-space : 256-512
The fast winner algorithm gave as the same winner as in the full search for sub-space dimension detween 350-512.
With the that techique the above timings were achived in finding the “global” and the “tentative” winner.
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Dime nsion diffe re nce
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The ANN we used consited of nearly 650 neurons arranged on a hexagonal lattice. That topology was selected for visualization reasons.
Nearly 6500 feature vectors were clustered in the training phase with the above network.
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MSE
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(23,14) nodebackpackborncalahorracitihighlandhuelvamarrimountainnicepseudostudentvictor
(24,15) nodeanayaandovbasqubeautibuilddoricextremadurafugoddessibizamagicmonasteriopulpitstonevisit
(25,15) nodearrebatacapabedroomdancmobilorleanpeplostreettherapeuttornotour
(22,4) nodeartworkimpoverishmuseopersonalisrainerspindleruvtourismverandavouli
(1,13) nodeclarksburgdiversglasgowhumpmonasteripartportugromanromntel
(11,15) nodeabdularcobelmacocremefrescogotgothiclitermasstouristmedievmournpalacriunionruasplendid
(13,14) nodeamericancentralmontrealperchprocessproducraymondromantsciencservtriumphalunusuupgradvenic
(9,13) nodeburjasotcatacombcentrcidcomfortculminhistormissnovembpelagiopiedrapridesilesiastit
Some of the characteristic nodes on the network
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Future objectives
Work done so far
