On the geodesic distance in shapes K-means clustering

Abstract : Using Information Geometry tools, we represent landmarks of a complex shape as probability densities in a statistical manifold. Then, in the setting of shapes clustering through a K-means algorithm, we evaluate the discriminative power of two different shapes distances. The first, derived from Fisher-Rao metric, is related with the minimization of information in the Fisher sense and the other is derived from the Wasserstein distance which measures the minimal transportation cost.
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Entropy, MDPI, 2018, Special Issue Selected Papers from 4th International Electronic Conference on Entropy and Its Applications, 20 (9), pp 647. 〈10.3390/e20090647〉
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Soumis le : jeudi 4 octobre 2018 - 11:11:28
Dernière modification le : lundi 10 décembre 2018 - 16:14:08

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Stefano Gattone, Angela De Sanctis, Stéphane Puechmorel, Florence Nicol. On the geodesic distance in shapes K-means clustering. Entropy, MDPI, 2018, Special Issue Selected Papers from 4th International Electronic Conference on Entropy and Its Applications, 20 (9), pp 647. 〈10.3390/e20090647〉. 〈hal-01852144v2〉

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