This thesis gathers some contributions to statistical pattern recognition particularly targeted
at problems in which the feature vectors are high-dimensional. Three pattern recognition
scenarios are addressed, namely pattern classification, regression analysis and score fusion.
For each of these, an algorithm for learning a statistical model is presented. In order to
address the difficulty that is encountered when the feature vectors are high-dimensional,
adequate models and objective functions are defined. The strategy of learning simultaneously
a dimensionality reduction function and the pattern recognition model parameters is shown to
be quite effective, making it possible to learn the model without discarding any discriminative
information. Another topic that is addressed in the thesis is the use of tangent vectors as
a way to take better advantage of the available training data. Using this idea, two popular
discriminative dimensionality reduction techniques are shown to be effectively improved. For
each of the algorithms proposed throughout the thesis, several data sets are used to illustrate
the properties and the performance of the approaches. The empirical results show that the
proposed techniques perform considerably well, and furthermore the models learned tend to
be very computationally efficient. / Villegas Santamaría, M. (2011). Contributions to High-Dimensional Pattern Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10939 / Palancia
| Identifer | oai:union.ndltd.org:upv.es/oai:riunet.upv.es:10251/10939 |
| Date | 20 May 2011 |
| Creators | Villegas Santamaría, Mauricio |
| Contributors | Paredes Palacios, Roberto, Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació |
| Publisher | Universitat Politècnica de València |
| Source Sets | Universitat Politècnica de València |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis, info:eu-repo/semantics/acceptedVersion |
| Source | Riunet |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, info:eu-repo/semantics/openAccess |
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