Article ID Journal Published Year Pages File Type
535999 Pattern Recognition Letters 2011 9 Pages PDF
Abstract

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.

► We propose a general incremental framework for spectral embedding methods. ► Based on the framework, the Incremental Hessian LLE (IHLLE) algorithm is proposed. ► The efficiency, accuracy and robustness of IHLLE are evaluated by simulations.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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