Abstract:Dimensionality reduction is among the keys in many fields, most of the traditional method can be categorized as local or global ones. In this paper, we consider the dimension reduction problem with prior information is available, namely, semi-supervised dimension reduction. A new dimension reduction method that can explore both the labeled and unlabeled information in the dataset is proposed. The labeled data points are used to maximize the separability between different classes and the unlabeled data points a… Show more
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