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Feature selection is one of the most important dimension reduction techniques for its efficiency and interpretation. Since practical data in large sca

In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios i

In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficie

Many pattern analysis and data mining problems have witnessed high-dimensional data represented by a large number of features, which are often redunda

Sparse learning has been proven to be a powerful technique in supervised feature selection, which allows to embed feature selection into the classific