Incremental Manifold Learning Via Tangent Space Alignment [chapter]

Xiaoming Liu, Jianwei Yin, Zhilin Feng, Jinxiang Dong
2006 Lecture Notes in Computer Science  
Several algorithms have been proposed to analysis the structure of high-dimensional data based on the notion of manifold learning. They have been used to extract the intrinsic characteristic of different type of high-dimensional data by performing nonlinear dimensionality reduction. Most of them operate in a "batch" mode and cannot be efficiently applied when data are collected sequentially. In this paper, we proposed an incremental version (ILTSA) of LTSA (Local Tangent Space Alignment), which
more » ... is one of the key manifold learning algorithms. Besides, a landmark version of LTSA (LLTSA) is proposed, where landmarks are selected based on LASSO regression, which is well known to favor sparse approximations because it uses regularization with l 1 norm. Furthermore, an incremental version (ILLTSA) of LLTSA is also proposed. Experimental results on synthetic data and real word data sets demonstrate the effectivity of our algorithms.
doi:10.1007/11829898_10 fatcat:nzhlssfuqrat5na723zqfg2d6m