Combining the ideas of maximum variance unfolding(MVU)and Laplacian eigenmaps,a new manifold learning algorithm is proposed,called distinguishing variance embeddings(DVE).DVE globally unfolds the manifold by maximizing the distances between far points,and faithfully preserves the local neighborhood with the distance-sum-constraint on the neighbors.DVE can be viewed as a variance of MVU that relaxes the strict distance-preserving constraints on the neighbors.We illustrate the algorithm on the easily visualiz...