2 research outputs found

    A new feature extraction method based on clustering for face recognition

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    Part 13: Feature Extraction - MinimizationInternational audienceWhen solving a pattern classification problem, it is common to apply a feature extraction method as a pre-processing step, not only to reduce the computation complexity but also to obtain better classification performance by reducing the amount of irrelevant and redundant information in the data. In this study, we investigate a novel schema for linear feature extraction in classification problems. The method we have proposed is based on clustering technique to realize feature extraction. It focuses in identifying and transforming redundant information in the data. A new similarity measure-based trend analysis is devised to identify those features. The simulation results on face recognition show that the proposed method gives better or competitive results when compared to conventional unsupervised methods like PCA and ICA

    Feature Clustering based MIM for a New Feature Extraction Method

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    In this paper, a new unsupervised Feature Extraction appoach is presented, which is based on feature clustering algorithm. Applying a divisive clustering algorithm, the method search for a compression of the information contained in the original set of features. It investigates the use of Mutual Information Maximization (MIM) to find appropriate transformation of clusterde features. Experiments on UCI datasets show that the proposed method often outperforms conventional unsupervised methods PCA and ICA from the point of view of classification accuracy
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