Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost effectiveness (linearization), ...

Subspace learning (SL) plays a key role in various learning tasks, especially those with a huge feature space. When processing multiple high-dimensional learning tasks simultaneously, it is of great ...

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