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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ARISTOTLE UNIVERSITY OF THESSALONIKI

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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-5

0

5

10

15

20

25

Dimension difference

(Sec

) Fast search

Full search

Differences

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ARISTOTLE UNIVERSITY OF THESSALONIKI

xi

mi

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

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97

Iterations

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