面向等维独立多流形的增量学习算法IMM-ISOMAP
首发时间:2016-12-01
摘要:流形学习是机器学习与数据挖掘领域的一个重要研究方向。其经典算法总是假设高维数据批量存在于单一流形,且不能有效处理增量出现的高维多流形数据。本文针对等维独立多流形提出一种增量学习算法IMM-ISOMAP。该算法首先对新样本计算动态邻域,通过扩展切空间的方法将新样本依次划分到各子流形,实现对新样本的分类并计算最终低维嵌入。实验结果表明,该算法可以有效地应用于人造数据和实际图像数据。
关键词: 流形学习; 增量学习; 等维独立多流形; 动态邻域; 切空间
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The incremental learning algorithm IMM-ISOMAP for well-separated multi-manifolds with same intrinsic dimension
Abstract:Manifold learning is an important research direction in the field of machine learning and data mining. The classical algorithm always assumes that the high dimensional batched data exists in a single manifold, and can not effectively deal with the high dimensional manifold data. In this paper, we propose an incremental learning algorithm IMM-ISOMAP for equal dimension independent multi-manifolds. Firstly, the algorithm computes the dynamic neighborhood of the new sample, and divides the new samples into sub-manifolds by extending the tangent space, and then realizes the classification of the new samples and calculates the final low dimensional embedding. Experimental results show that the proposed algorithm can be effectively applied to artificial data and real image data.
Keywords: Manifold learning Incremental learning Equal dimension independent multi-manifolds Dynamic neighborhood Tangent Space
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