Feature Selection With Redundancy-Constrained Class Separability
Scatter-matrix-based class separability is a simple and efficient feature selection criterion in the literature. However, the conventional trace-based formulation does not take feature redundancy into account and is prone to selecting a set of discriminative but mutually redundant features. In this brief, we first theoretically prove that in the context of this trace-based criterion the existence of sufficiently correlated features can always prevent selecting the optimal feature set. Then, on...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Neural Networks|
|01_Zhou_Feature_Selection_With_2010.pdf||534.67 kB||Adobe PDF||Request a copy|
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