Image analysis with regularized Laplacian eigenmaps

Many classes of image data span a low dimensional nonlinear space embedded in the natural high dimensional image space. We adopt and generalize a recently proposed dimensionality reduction method for computing approximate regularized Laplacian eigenmaps on large data sets and examine for the first time its application in a variety of image analysis examples. These experiments demonstrate the potential of regularized Laplacian eigenmaps in developing new learning algorithms and improving performance of existing systems.