In this paper, we suggest a computationally superior mechanism to solve the problem. Rather than define the matrix K with the whole data set and compute the ...

The PRS is a way of reducing the number of training vectors while simultaneously insisting that the classifiers built on the reduced design set perform as well, ...

Recently, using the kPCA, kernel-based subspace methods, such as the subspace method in Hilbert space (SHS) [5], kernel-based nonlinear subspace (KNS) method [6] ...

In this paper, we suggest a computationally superior mechanism to solve the problem. Rather than define the matrix K with the whole data set and compute the ...

... kernel-based nonlinear subspace methods, in. Section 3 we show how large data sets of training patterns can be reduced into smaller prototype sets by ...

The proposed mechanism dramatically reduces the computation time without sacrificing the classification accuracy for samples involving real-life data sets ...

Semantic Scholar extracted view of "On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods" by Sang-Woon Kim et al.

Abstract. The subspace method of pattern recognition is a classification technique in which pattern classes are specified in terms of linear subspaces ...

In this method, we produce initial prototypes for classification based on self-organization and optimize the prototypes using a multi-layer neural network.

Abstract: In kernel-based nonlinear subspace (KNS) methods, the length of the projections onto the principal component directions in the feature space, ...