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In manipulating data such as in supervised or unsupervised learning, the authors need to extract new features from the original features for the purpose of reducing the dimension of feature space and achieving better performance. In this paper, they investigate a novel schema for unsupervised feature extraction for classification problems. They based their method on clustering to achieve feature extraction. A new similarity measure based on trend analysis is devised to identify redundant information in the data. Clustering is then performed on the feature space. Once groups of similar features are formed, linear transformation is realized to extract a new set of features.
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