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Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data
UNIFIER explores the local structure of multi-view data by adaptively learning similarity-induced graphs from both the sample and feature spaces.
C$^{2}$IMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection
Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views.
Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection
Other existing methods choose the discriminative features with low redundancy by constructing the graph matrix on the original feature space.
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