Bilateral Two-Dimensional Locality Preserving Projections

In this paper, we investigate locality preserving projections (LPP) in two-dimensional sense. Recently, LPP was proposed for dimensionality reduction, which can detect the intrinsic manifold structure of data and preserve the local information. When image data are concerned, they are often vectorized for LPP. However, the dimension of image data is usually very high, LPP can´t be implemented due to singularity of matrix. We propose two methods for image dimensionality reduction: two-dimensional LPP (2DLPP) and bilateral two-dimensional LPP (B2DLPP), which are based directly on 2D image matrices rather than 1D vectors as LPP does. Experiments are conducted on the ORL face database, which shows higher recognition performance of the proposed methods.