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Abstract
In many machine learning and data mining tasks, it can't achieve the best semi-supervised learning result if only use side-information. So, a local and global preserving based semi-supervised dimensionality reduction (LGSSDR) method is proposed in this paper. LGSSDR algorithm can not only preserve the positive and negative constraints but also preserve the local and global structure of the whole data manifold in the low dimensional embedding subspace. Besides, the algorithm can compute the transformation matrix and handle unseen samples easily. Experimental results on several datasets demonstrate the effectiveness of this method.
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