Efficient Spectral Feature Selection with Minimum Redundancy
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]
|Collections||ANU Research Publications|
|Source:||Proceedings of National Conference on Artificial Intelligence (AAAI 2010)|
|01_Zhao_Efficient_Spectral_Feature_2010.pdf||301.12 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.