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Classical dictionary learning algorithms that rely on a single source of information have been successfully used for the discriminative tasks. However, exploiting multiple sources has demonstrated its effectiveness in solving challenging real-world situations. We propose a new framework for feature fusion to achieve better classification performance as compared to the case where individual sources...
This paper presents a hybrid feature extraction framework based on two diverse optimization problems in aspects of risk and independence to extract features for higher classification performance. The risk minimization as a supervised approach pursues maximum generalization capability among data to directly improve classification performance, whereas the independence maximization process as an unsupervised...
An intrusion detection system (IDS) detects illegal manipulations of computer systems. In intrusion detection systems, feature reduction, including feature extraction and feature selection, plays an important role in a sense of improving classification performance and reducing the computational complexity. Feature reduction is even more important when online detection, which means less computational...
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