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.