By Erich Gröner; Dieter Jung; Martin Maass
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We note that there are also semi-supervised approach to LDA as a dimensionality reduction technique. As we consider LDA as a classifier, we do not discuss these approach here except for the work in [10], which comes close to ours in some sense. It aims to improve the estimates of particular parameters by including unlabeled data in the estimation procedure as well. It notes that the Fisher criterion, which typically employs the between-class and within-class covariance can also be expressed in such a way that the total covariance matrix replaces one of the other two (cf.
Future work should include the study of more sophisticated self-labelling methods in order to improve the poor self-training results obtained. As for the active training, different approaches to the one utilized exist and should also be studied [15,14,10,17]. Further, as the utilized datasets were composed of acted speech segments, future work should include the study of natural data as well as a deeper knowledge of the representative characteristics of each different emotion. 30 J. Esparza, S. Scherer, and F.
The iterations are repeated for all the unseen data-points. When the new fuzzy labels are generated, the SVMs are trained in a supervised style, assuming that the automatically generated labels are correct, leading to a semi-supervised approach. In order to control the amount of error introduced by the automatically generated labels, they are processed discrimination process with a pivot parameter p. Labels with a confidence higher than p are used for training the SVMs and those with a lower confidence are discarded.