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Dynamic graph learning for spectral feature selection
Ist Teil von
Multimedia tools and applications, 2018-11, Vol.77 (22), p.29739-29755
Ort / Verlag
New York: Springer US
Erscheinungsjahr
2018
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
Previous spectral feature selection methods generate the similarity graph via ignoring the negative effect of noise and redundancy of the original feature space, and ignoring the association between graph matrix learning and feature selection, so that easily producing suboptimal results. To address these issues, this paper joints graph learning and feature selection in a framework to obtain optimal selected performance. More specifically, we use the least square loss function and an
ℓ
2,1
-norm regularization to remove the effect of noisy and redundancy features, and use the resulting local correlations among the features to dynamically learn a graph matrix from a low-dimensional space of original data. Experimental results on real data sets show that our method outperforms the state-of-the-art feature selection methods for classification tasks.