HIGHLIGHTS
- who: Jun Cai and collaborators from the College of Communications Engineering, Army Engineering University of PLA, Nanjing, China have published the article: Unsupervised and Supervised Feature Selection for Incomplete Data via L2,1-Norm and Reconstruction Error Minimization, in the Journal: (JOURNAL)
- what: The authors propose and feature selection methods for use with incomplete further introducing an 1 and a method. SFS algorithms identify the relevant features to best achieve the goal of the supervised model, whereas UFS algorithms are interpretable, since it is usually difficult to obtain the labels of samples in practical applications . . .
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