Feature Extraction Based on Low Rank Representation Linear Preserving Projections
Yang Guo-lian · 2015
For preserving the low rank properties the same,we proposed an algorithm,called linear preserving projection based on low rank representations(LLRLPP),to reduce the dimension of data.It can preserve the low rank properties of the original data space in the resulting low dimensional embedding subspace and correctly learn the low-dimensional subspace.Through constructing two different low rank representation model,the low rank weights of representing different structural characteristics are revealed.Then the low-dimensional subspace of the original high-dimensional data is obtained by preserving such low rank weight relationship.The effectiveness of the proposed method is verified on two face databases(ORL,Yale)with the traditional algorithms.