Haptic handwritten signatures: the effect of deconcentrated dissimilarities on manifold extraction

DOIResolve DOI: http://doi.org/10.1109/HAVE.2015.7359450
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TypeArticle
Proceedings title2015 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE)
Conference2015 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), October 11 2015, Ottawa, ON, Canada
ISBN978-1-4673-9175-7
Pages16
AbstractThe use of a haptic-based handwritten signatures has an intrinsic biometric nature and an important potential in user identification/authentication because it incorporates tactile information. However, in order to exploit this potential for constructing decision systems, it is necessary to gain an appropriate understanding of the internal structure of the data, which in relational representations tend to be very highly dimensional. Most machine learning techniques i) are affected by the curse of dimensionality, ii) use algorithms involving distances (usually Euclidean), but in high dimensional spaces they suffer from the concentration phenomenon. This paper explores the behavior of different strategies for distance deconcentration of haptic data when used for nonlinear unsupervised mappings into low dimensional spaces. An aposteriori use of class information shows that deconcentration transformations improve class cohesion and separation, which can improve the performance of machine learning algorithms.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23000055
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Record identifieraa6df42c-aab4-4455-80bb-daec7f74e5d3
Record created2016-06-01
Record modified2016-06-01
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