Kernel Domain Description with Incomplete Data: Using Instance-Specific Margins to Avoid Imputation

Abstract

We present a method of performing kernel space domain description of a dataset with incomplete entries without the need for imputation, allowing kernel features of a class of data with missing features to be rigorously described. This addresses the problem that absent data completion is usually required before kernel classifiers, such as support vector… (More)
DOI: 10.1109/ICPR.2010.716

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@article{Gripton2010KernelDD, title={Kernel Domain Description with Incomplete Data: Using Instance-Specific Margins to Avoid Imputation}, author={Adam Gripton and Weiping Lu}, journal={2010 20th International Conference on Pattern Recognition}, year={2010}, pages={2921-2924} }