Feature Selection with Kernel Class Separability
Download (2.84 MB)
-
Altmetric Citations
Description
Classification can often benefit from efficient feature selection. However, the presence of linearly nonseparable data, quick response requirement, small sample problem and noisy features makes the feature selection quite challenging. In this work, a class separability criterion is developed in a high-dimensional kernel space, and feature selection is performed by the maximization of this criterion. To make this feature selection approach work, the issues of automatic kernel parameter tuning,...[Show more]
Collections | ANU Research Publications |
---|---|
Date published: | 2008 |
Type: | Journal article |
URI: | http://hdl.handle.net/1885/36345 |
Source: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
DOI: | 10.1109/TPAMI.2007.70799 |
Download
File | Description | Size | Format | Image |
---|---|---|---|---|
01_Wang_Feature_Selection_with_Kernel_2008.pdf | 2.84 MB | Adobe PDF |
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.
Updated: 17 November 2022/ Responsible Officer: University Librarian/ Page Contact: Library Systems & Web Coordinator