Global Mapping Analysis: Stochastic Gradient Algorithm in SSTRESS and Classical MDS Stress

摘要：

We propose a new on-line processing algorithm for solving multidimensional scaling, which is named global mapping analysis (GMA.) In GMA, stochastic gradient algorithm is applied to minimizing two well-known MDS criteria: SSTRESS 15 and classical MDS stress 16, 6. By use of GMA on the two criteria, the required memory space is reduced from the square order of the number of signals to the linear one. We also show that GMA on classical MDS is equivalent to Ojas symmetrical PCA network rule 12, and it always converges to the global optimum. The numerical experiments on 1000000 controlled signals showed that weakly correlated signals are clustered clearly by GMA.

作者: Yoshitatsu Matsuda Kazunori Yamaguchi

作者单位: Laboratory Department of Graphic and Computer Science College of Arts and Sciences The University of Tokyo 3-8-1, Komaba, Meguro-ku, Tokyo, 153-8902, Japan

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 140-145

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）