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NEPv Approach for Optimization on Stiefel Manifold with the $(2,1)$-norm Regularization
Row-sparse projection provides a useful tool in machine learning (ML) when it comes to, for example, feature selection, aiming to choose most relevant features for various ML objectives. One way to seek a high quality row-sparse projection is to combine an ML objective, such as the ones for PCA, LDA
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