Half-Quadratic Minimization for Unsupervised Feature Selection on Incomplete Data.
IEEE Transactions on Neural Networks and Learning Systems（2021）
摘要
Unsupervised feature selection (UFS) is a popular technique of reducing the dimensions of high-dimensional data. Previous UFS methods were often designed with the assumption that the whole information in the data set is observed. However, incomplete data sets that contain unobserved information can be often found in real applications, especially in industry. Thus, these existing UFS methods have a...
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关键词
Feature extraction,Data models,Minimization,Data analysis,Robustness,Analytical models,Machine learning
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