【摘要】 流形学习是近年来机器学习与认知科学中的一个新的研究热点,其本质在于根据有限的离散样本学习和发现嵌入在高维空间中的低维光滑流形,从而揭示隐藏在高维数据中的内在低维结构,以实现非线性降维或者可视化。介绍了几种主要的流形学习算法,分析了它们的优势与不足,总结了流形学习方法中需要解决的若干问题及其研究现状,并展望了流形学习未来的研究前景。更多还原

【Abstract】 Manifold learning is a newer research direction of machine learning and congnitive science in recent years,its essence is to find out the low dimensional manifold hidden in high dimensional space though learning discrete samples,and get the hidden dimensional structure of the high dimensional data to realize non-linear dimension reduction.The paper introduced some manifold learning algorithms,summarized some problems of manifold learning and its research status,and discussed the prospect of manifold learning.更多还原