Adaptive Weighted Sparse Principal Component Analysis for Robust Unsupervised Feature Selection.
IEEE Transactions on Neural Networks and Learning Systems(2020)
摘要
Current unsupervised feature selection methods cannot well select the effective features from the corrupted data. To this end, we propose a robust unsupervised feature selection method under the robust principal component analysis (PCA) reconstruction criterion, which is named the adaptive weighted sparse PCA (AW-SPCA). In the proposed method, both the regularization term and the reconstruction er...
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关键词
Feature extraction,Principal component analysis,Image reconstruction,Correlation,Laplace equations,Computer science,Noise measurement
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