A multi-manifold semi-supervised Gaussian mixture model for pattern classification

作者：Xing Xianglei; Yu Yao; Jiang Hua; Du Sidan^*

来源：Pattern Recognition Letters, 2013, 34(16): 2118-2125.

DOI：10.1016/j.patrec.2013.08.005

• Multi-manifold learning
• Semi-supervised learning
• Gaussian mixture model

Semi-supervised Gaussian mixture model (SGMM) has been successfully applied to a wide range of engineering and scientific fields, including text classification, image retrieval, and biometric identification. Recently, many studies have shown that naturally occurring data may reside on or near manifold structures in ambient space. In this paper, we study the use of SGMM for data sets containing multiple separated or intersecting manifold structures. We propose a new multi-manifold regularized, semi-supervised Gaussian mixture model (M2SGMM) for classifying multiple manifolds. Specifically, we model the data manifold using a similarity graph with local and geometrical consistency properties. The geometrical similarity is measured by a novel application of local tangent space. We regularize the model parameters of the SGMM by incorporating the enhanced Laplacian of the graph. Experiments demonstrate the effectiveness of the proposed approach.

• 出版日期2013-12-1
• 单位南京大学