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This paper investigates the use of the Compressive Sensing (CS) technique to the classification issue. In this context, CS is used as a means to probe the nonlinear manifold on which faces under various illumination effects reside. The scheme of randomly sampled faces (Randomfaces) with nearest neighbor classifier are compared with two classical feature extraction approaches, as Eigenfaces and Fisherfaces...
This paper presents a methodology for learning taxonomic relations from a set of documents that each explain one of the concepts. Three different feature extraction approaches with varying degree of language independence are compared in this study. The first feature extraction scheme is a language-independent approach based on statistical keyphrase extraction, and the second one is based on a combination...
The efficiency of the feature extraction approaches which are based on the statistical analysis of the training samples decreases by the small sample size. Validation of the statistical analysis depends on the number of training samples which is usually small in face recognition problems. In this paper, a statistical analysis in DCT domain is used for feature extraction, and the effect of small sample...
This paper compares several feature extraction approaches based on Gaussian mixture model (GMM) for support vector machine (SVM) in text-independent speaker verification. Because of excellent scalability, GMM can be used to extract fixed number of typical feature vectors from various length speech data. Experiments with different GMM-based features in SVM speaker verification system were performed...
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