Distinguishing variance embedding

作者：Wang Qinggang^*; Li Jianwei; Wang Xuchu

来源：Image and Vision Computing, 2010, 28(6): 872-880.

DOI：10.1016/j.imavis.2009.11.003

• Manifold learning
• Dimensionality reduction
• Maximum variance unfolding
• Laplacian eigenmaps
• Variance analysis

Nonlinear dimensionality reduction is a challenging problem encountered in a variety of high dimensional data analysis. Based on the different geometric intuitions of manifolds, maximum variance unfolding (MVU) and Laplacian eigenmaps are designed for detecting the different aspects of data set. In this paper, combining the ideas of MVU and Laplacian eigenmaps, we propose a new nonlinear dimensionality reduction method called distinguishing variance embedding (DVE), which unfolds the data manifold by maximizing the global variance subject to the proximity relation preservation constraint originated in Laplacian eigenmaps. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on actual images of faces, handwritten digits, and rotating objects.

• 出版日期2010-6
• 单位重庆大学