Abstract
Researches on current feature extraction methods are mainly based on two ways. One originates from geometric properties of high-dimensional datasets and attempt to extract fewer features from the original data space according to a certain criterion. The other originates from dimension reduction deviation and tries to make the deviation between data before and after dimension reduction be as small as possible. However, there exists almost no any study about them from the perspective of the scatter change of a dataset. Based on Parzen window density estimator, the relevant feature extraction methods are thoroughly revisited from a new perspective and the relations between Parzen window and LPP and LDA are built in this paper.
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Liu, Zb., Zhang, J., Song, Wa. (2016). From Parzen Window Estimation to Feature Extraction: A New Perspective. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_3
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DOI: https://doi.org/10.1007/978-3-319-46257-8_3
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