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arXiv · 0810.4611

Learning Isometric Separation Maps

Nikolaos Vasiloglou·Alexander G. Gray·David V. Anderson
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Abstract

Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensional spaces, often revealing the true intrinsic dimension. In this paper we show how to also incorporate supervised class information into an MVU-like method without breaking its convexity. We call this method the Isometric Separation Map and we show that the resulting kernel matrix can be used as a binary/multiclass Support Vector Machine-like method in a semi-supervised (transductive) framework. We also show that the method always finds a kernel matrix that linearly separates the training data exactly without projecting them in infinite dimensional spaces. In traditional SVMs we choose a kernel and hope that the data become linearly separable in the kernel space. In this paper we show how the hyperplane can be chosen ad-hoc and the kernel is trained so that data are always linearly separable. Comparisons with Large Margin SVMs show comparable performance.

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Nikolaos Vasiloglou, Alexander G. Gray, David V. Anderson. 2009-04-15. Learning Isometric Separation Maps. https://arxiv.org/abs/0810.4611

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