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Neighborhood preserving embedding (NPE) has been widely used to learn the intrinsic structure of data. However, it may impair the local topology and ignore the diversity of data. In this paper, we present a dimensionality reduction approach, namely discriminant neighborhood structure embedding (DNSE). DNSE constructs an adjacency graph to characterize the diversity of data and combines NPE to learn...
Nonlinear dimensionality reduction (DR) algorithms can reveal the intrinsic characteristic of the high dimensional data in a succinct way. However, most of these methods suffer from two problems. First, the incremental dimensionality reduction problem, which means the algorithms cannot compute the embedding of new added data incrementally. Second, the high dimensional data reconstruction problem,...
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