document: Efficient kernel feature extraction for massive data sets Maximum margin discriminant analysis ( MMDA ) was proposed that uses the margin idea for feature extraction . It often outperforms traditional methods like kernel principal component analysis ( KPCA ) and kernel Fisher discriminant analysis ( KFD ) . However , as in other kernel methods , its time complexity is cubic in the number of training points m , and is thus computationally inefficient on massive data sets . In this paper , we propose an ( 1 + ε ) 2-approximation algorithm for obtaining the MMDA features by extending the core vector machines . The resultant time complexity is only linear in m , while its space complexity is independent of m. Extensive comparisons with the original MMDA , KPCA , and KFD on a number of large data sets show that the proposed feature extractor can improve classification accuracy , and is also faster than these kernel-based methods by more than an order of magnitude .
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