| DOI | Resolve DOI: https://doi.org/10.1109/HAVE.2015.7359450 |
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| Author | Search for: Valdes, Julio J.1; Search for: Alsulaiman, Fawaz A.; Search for: El Saddik, Abdulmotaleb |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | 2015 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), October 11 2015, Ottawa, ON, Canada |
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| Abstract | The 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. |
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| Publication date | 2015 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23000055 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | aa6df42c-aab4-4455-80bb-daec7f74e5d3 |
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| Record created | 2016-06-01 |
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| Record modified | 2020-04-22 |
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