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Latent Complete Row Space Recovery for Multi-view Subspace Clustering
LCRSR is able to recover the row space of the latent representation, which not only carries complete information from multiple views but also determines the subspace membership under certain conditions.
Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multi-view Learning
More interestingly, some linear transformation based complete multi-view methods can be adapted to IML directly with the guidance of the framework.
Effective Discriminative Feature Selection with Non-trivial Solutions
In this paper, a feature selection method is proposed by combining the popular transformation based dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity regularization.
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