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Efficient Spectral Feature Selection with Minimum Redundancy

Zhao, Zheng; Wang, Lei; Liu, Huan


Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features individually and cannot identify redundant features. Since redundant features can have significant...[Show more]

CollectionsANU Research Publications
Date published: 2010
Type: Conference paper
Source: Proceedings of National Conference on Artificial Intelligence (AAAI 2010)


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