Incremental manifold learning by spectral embedding methods

作者：Li, Housen^*; Jiang, Hao; Barrio, Roberto; Liao, Xiangke; Cheng, Lizhi; Su, Fang

来源：Pattern Recognition Letters, 2011, 32(10): 1447-1455.

DOI：10.1016/j.patrec.2011.04.004

• Manifold learning
• Incremental learning
• Dimensionality reduction
• Spectral embedding methods
• Hessian eigenmaps

Recent years have witnessed great success of manifold learning methods in understanding the structure of multidimensional patterns. However, most of these methods operate in a batch mode and cannot be effectively applied when data are collected sequentially. In this paper, we propose a general incremental learning framework, capable of dealing with one or more new samples each time, for the so-called spectral embedding methods. In the proposed framework, the incremental dimensionality reduction problem reduces to an incremental eigen-problem of matrices. Furthermore, we present, using this framework as a tool, an incremental version of Hessian eigenmaps, the IHLLE method. Finally, we show several experimental results on both synthetic and real world datasets, demonstrating the efficiency and accuracy of the proposed algorithm.

• 出版日期2011-7-15
• 单位中国人民解放军国防科学技术大学