Low-rank preserving embedding

In this paper, we consider the problem of linear dimensionality reduction with the novel technique of low-rank representation, which is a promising tool of discovering subspace structures of given data. Existing approaches based on graph embedding usually capture structure of data via stacking the local structure of each datum, such as neighborhood graph, ℓ1-graph and ℓ2-graph. Yet they lack explicit discrimination between those local structures...