Extreme Learning Machines For Approximating Nonlinear Dimensionality Reduction Mappings: Application To Haptic Handwritten Signatures

The abundance of computing and mobile devices makes the problem of user identification and verification an essential requirement for many applications. Haptics devices include the sense of touch in the form of kinesthetic and tactile feedback which provide additional features within handwritten signatures. However, they generate high dimensional data and dimensionality reduction techniques become useful for data mining, machine learning and visualization.Nonlinear transformations have been used for this, but in present day scenarios (Big Data, the Internet of Things, massive data streams, etc.) the computation becomes more complex, time consuming or impractical. Moreover, the relationships between the features of the original and the target spaces are more difficult to uncover. Extreme Learning Machines (ELM) are used for approximating nonlinear manifold learning methods in two ways: as a functional representation for implicit methods, and as simpler surrogate models for explicit mapping techniques. In the context of Haptic handwritten signatures, five implicit and explicit nonlinear transformation methods are investigated. In all cases it was found that ELM approximations to the mappings obtained with the original methods exhibit very good behavior and can be used either as functional representations for the implicit methods or as simpler surrogate models for explicit techniques.