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In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples...
In this paper we propose a new approach to theoretically analyze compressive sensing directly from the randomly sampling matrix ?? instead of a certain recovery algorithm. For simplifying our analyses, we consider x as a binary sparse source with independent and identical distribution P??, where the transform ?? is omitted as an identity matrix. For convenient analysis, we reform the tree structure...
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