Authors:
Wissal Drira
and
Faouzi Ghorbel
Affiliation:
National School of Computer Sciences and University of Manouba, Tunisia
Keyword(s):
Face Classification, Bayes, Feature Extraction, Reduction Dimension, L2 Probabilistic Dependence Measure.
Abstract:
In the supervised context, we intend to introduce a system which is composed of a series of novel and efficient algorithms that is able to realize a non parametric Bayesian classifier for high dimension. The proposed system tries to search for the best discriminate sub space in the mean of the minimum of the probability error of classification which is computed by using a modified kernel estimate of the conditional probability density functions. Therefore, Bayesian classification rule is applied in the reduced sub space. Such heuristic consists of four tasks. First, we maximize a novel estimate of the quadratic measure of the probabilistic dependence in order to realize multivariate extractors resulting from a number of different initializations of a given numerical optimizing procedure. Second, an estimation of the miss classification error is computed for each solution by the kernel estimate of the conditional probability density functions with the optimal band-with parameter in th
e sense of the Mean Integrate Square Error (MISE) which is obtained with the Plug in algorithm. Third, the sub space which presents the minimum of the miss classification values is thus chosen. After that, the Bayesian classification rule is operated in the reduced sub space with the optimal MISE of the modified kernel estimate. Finally, different algorithms will be applied to a base of images in grayscale representing classes of faces, showing its interest in the case of real data.
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