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• W3215736224 abstract "Supervised subspace projection technology is a major method for dimensionality reduction in pattern recognition. At present, most supervised subspace projection algorithms are derived from the multi-dimensional extended version of Fisher linear discriminant analysis (FDA), also known as Multi-dimensional Fisher discriminant analysis (MD-FDA). However, MD-FDA needs to be improved further because the projection vectors in the noise-subspace cannot be sorted and the ill-condition of the within-class scatter matrix may cause severe numerical instabilities. Generalized discriminant component analysis (GDCA), the generalization of MD-FDA, together with its kernelization forms are proposed and correspondingly rigorous mathematical proofs are detailed in this paper. By virtue of 5 validation data sets derived from UCI Machine Learning Repository and our laboratory, the theoretical validity and technical advantages of GDCA as well as its kernelization forms are verified, and the effectiveness of the newly proposed method is demonstrated in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms." @default.
• W3215736224 created "2021-12-06" @default.
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• W3215736224 date "2022-04-01" @default.
• W3215736224 modified "2023-10-02" @default.
• W3215736224 title "Supervised dimensionality reduction technology of generalized discriminant component analysis and its kernelization forms" @default.
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• W3215736224 doi "https://doi.org/10.1016/j.patcog.2021.108450" @default.
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