Abstract:
This paper discusses semi-supervised learning instance and feature co-selection. Co-selection is harder when data contain labeled and unlabeled examples from the same population.
sCOs, a unified framework, efficiently integrates labeled and unlabeled parts into semi-supervised co-selection. Sparse regularization and similarity preservation form the framework. It simultaneously selects the most relevant features and instances by assessing their usefulness.
We propose two efficient convex and nonconvex algorithms. This paper is the first to use nonconvex penalties for semi-supervised learning task co-selection. sCOs is validated and compared to state-of-the-art methods using benchmark datasets.
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