文献类型：期刊文献

英文题名：MANIFOLD LEARNING AND VISUALIZATION BASED ON DYNAMIC SELF-ORGANIZING MAP

作者：Shao, Chao[1];Wan, Chunhong[1];Hu, Haitao[1]

第一作者：邵超

通讯作者：Shao, C[1]

机构：[1]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450002, Peoples R China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450002, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

年份：2015

卷号：25

期号：2

起止页码：175-188

外文期刊名：NEURAL NETWORK WORLD

收录：;EI(收录号：20163802818027);Scopus(收录号：2-s2.0-84987768454);WOS:【SCI-EXPANDED(收录号:WOS:000354664000005)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant No.61202285, the Research Programme of Henan Fundamental and Advanced Technology of China under Grant No. 112300410201, and the Key Science & Technology Research Programme of Educational Commission of Henan Province of China under Grant No. 14B520020.

语种：英文

外文关键词：Manifold learning; self-organizing map; topological defect; neighborhood structure; robustness

摘要：For the data sampled from a low-dimensional nonlinear manifold embedded in a high-dimensional space, such as Swiss roll and S-curve, Self-Organizing Map (SOM) tends to get stuck in local minima and then yield topological defects in the final map. To avoid this problem and obtain more faithful visualization results, a variant of SOM, i.e. Dynamic Self-Organizing Map (DSOM), was presented in this paper. DSOM can dynamically increase the map size, as the training data set is expanded according to its intrinsic neighborhood structure, starting from a small neighborhood in which the data points can lie on or close to a linear patch. According to the locally Euclidean nature of the manifold, the map can be guided onto the manifold surface and then the global faithful visualization results can be achieved step by step. Experimental results show that DSOM can discover the intrinsic manifold structure of the data more faithfully than SOM. In addition, as a new manifold learning method, DSOM can obtain more concise visualization results and be less sensitive to the neighborhood size and the noise than typical manifold learning methods, such as Isometric Mapping (ISOMAP) and Locally Linear Embedding (LLE), which can also be verified by experimental results.