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论文类型：期刊论文

发表时间：2012-06-01

发表刊物：Advances in information Sciences and Service Sciences

收录刊物：EI、Scopus

卷号：4

期号：12

页面范围：370-377

摘要：Large dimensionality leads to intractable complexity to machine learning algorithms. ISOMAP is a typical manifold learning technique which extracts intrinsic low-dimensional structure from high dimensional data. Since the complexity of eigen-decomposition in ISOMAP is O(n 3), ISOMAP is coupled with Nystr?m method when it is used in large scale manifold learning. The landmark point set is an important factor for the approximation of Nystr?m method. In this paper, we present an incremental sampling scheme. Experimental results show that the Nystr?m extension with incremental sampling performs better than with random sampling.