19 May 2016 Discriminant power analyses of non-linear dimension expansion methods
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Most non-linear classification methods can be viewed as non-linear dimension expansion methods followed by a linear classifier. For example, the support vector machine (SVM) expands the dimensions of the original data using various kernels and classifies the data in the expanded data space using a linear SVM. In case of extreme learning machines or neural networks, the dimensions are expanded by hidden neurons and the final layer represents the linear classification. In this paper, we analyze the discriminant powers of various non-linear classifiers. Some analyses of the discriminating powers of non-linear dimension expansion methods are presented along with a suggestion of how to improve separability in non-linear classifiers.
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Seongyoun Woo, Seongyoun Woo, Chulhee Lee, Chulhee Lee, "Discriminant power analyses of non-linear dimension expansion methods", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740O (19 May 2016); doi: 10.1117/12.2224454; https://doi.org/10.1117/12.2224454


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