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- W2356785732 abstract "Dimensionality reduction is one of the common methods to preprocess astronomical spectral data.How to use label astronomical spectral data for conquering over-fitting of the dimensionality reduction process is an effective way to improve the effect of dimensionality reduction.In this paper,a dimension reduction method of astronomical spectrum data feature is presented by utilizing semi-supervised learning.Firstly,for the label Celestial spectral data,an uncertainty relation is established in which Fisher discriminant analysis and PCA can be selected variably.Secondly,the global optimization of semi-supervised dimensionality reduction is built.Dimensionality reduction results are calculated through the eigenvalue decomposition,so that the problem of over-fitting is solved in astronomical spectral data dimensionality reduction.In the end,the experimental results proved the validity of the method by using the hzqso and mstar astronomical spectral features line data sets." @default.
- W2356785732 created "2016-06-24" @default.
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- W2356785732 date "2012-01-01" @default.
- W2356785732 modified "2023-09-24" @default.
- W2356785732 title "Semi-supervised Dimension Reduction of Spectral Characteristic Based on Fisher Discriminant Analysis" @default.
- W2356785732 hasPublicationYear "2012" @default.
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