Abstract
Biometric face data are essentially high dimensional data and as such are susceptible to the well-known problem of the curse of dimensionality when analyzed using machine learning techniques. Research has shown that biometric face data are nonlinear in structure and, manifold learning methods are able to preserve the original non-linear structure of high dimensional data in lower dimensional space.
Manifold learning methods suffer from two problems. First the generalization problem and second, the classification problem. In this dissertation, we will present Graph Embedding and Semi-supervised Graph Embedding as approaches to advance the applicability of manifold learning methods in dimensionality reduction of biometric face data for the purpose of recognition.
Manifold learning methods suffer from two problems. First the generalization problem and second, the classification problem. In this dissertation, we will present Graph Embedding and Semi-supervised Graph Embedding as approaches to advance the applicability of manifold learning methods in dimensionality reduction of biometric face data for the purpose of recognition.
Original language | English |
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Qualification | Masters |
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Award date | 23 Sept 2016 |
Publication status | Unpublished - 2016 |