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Misclassification-Driven Sample Relabeling for Supervised Kernel Principal Component Analysis

Publication date: 24.03.2017

Schedae Informaticae, 2016, Volume 25, pp. 25-35

https://doi.org/10.4467/20838476SI.16.002.6183

Authors

,
Maciej Adamiak
Faculty of Geographical Sciences, University of Lodz
ul. Narutowicza 65, 90-131 Łódź, Poland
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Krzysztof Ślot
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Titles

Misclassification-Driven Sample Relabeling for Supervised Kernel Principal Component Analysis

Abstract

Abstract. Supervised kernel-Principal Component Analysis (S-kPCA) is a me thod for producing discriminative feature spaces that provide nonlinear decision regions, well-suited for handling real-world problems. The presented paper proposes a modification to the original S-kPCA concept, which is aimed at improving class-separation in resulting feature spaces. This is accomplished by identifying outliers (understood here as misclassified samples) and by an appropriate reformulation of the original S-kPCA problem. The proposed idea is to replace binary class labels that are used in the original method, by real-valued ones, derived using sample-relabeling scheme aimed at preventing potential data classification problems. The postulated concept has been tested on three standard pattern recognition datasets. It has been shown that classification performance in feature spaces derived using the introduced methodology improves by 4–16% with respect to the original S-kPCA method, depending on a dataset.

References

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Information

Information: Schedae Informaticae, 2016, Volume 25, pp. 25-35

Article type: Original article

Authors

Faculty of Geographical Sciences, University of Lodz
ul. Narutowicza 65, 90-131 Łódź, Poland

Published at: 24.03.2017

Article status: Open

Licence: None

Percentage share of authors:

Maciej Adamiak (Author) - 50%
Krzysztof Ślot (Author) - 50%

Article corrections:

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Publication languages:

English

Misclassification-Driven Sample Relabeling for Supervised Kernel Principal Component Analysis

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