Studies on Multimodal Information Processing Based Upon Fast Expectation-Maximization

基于快速期望最大化的多模态信息处理研究

• 批准号: 11680401
• 负责人: MATSUYAMA Yasuo
• 金额: $ 2.3万
• 依托单位: Waseda University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1999
• 资助国家: 日本
• 起止时间: 1999 至 2000
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11680401/
• 关键词: multimodal information processing; fast algorithm; α-EM algorithm; α-ICA algorithm; fMRI for brains; α-ICAアルゴリズム; 期待値最大化; 高速ICA; 感性処理処理

This project had the following two targets:(1) Investigation of new algorithms which give optimal structures measured by probabilistic and statistical performance,(2) Applications of the obtained algorithms to multimodal information sources which correspond to human signals.The first year was used to create a new class of information processing methods. In this phase, the following results were obtained:(a) A new class of expectation-maximization algorithm was found. This method was named the α-EM algorithm. The α-EM algorithm contains the traditional log-EM algorithm as a special case. The performance in speed outperforms the traditional log-EM method. This work received the Telecommunications Advancement Foundation Award.(b) The above method using the extended logarithm was found to be applicable to the independent component analysis which separates unknown source signals. This new method was named the α-ICA.In the last year, the above methods (a) and (b) were applied to multimodal information processing. Obtained results are(i) Motion estimation from optical flows,(ii) Estimation of living human brains' activities from functional magnetic images. It was found that there is an active area in the rear of the right hemisphere. This active area is asymmetric.As is explained above, this research project was ended with lots of viable results.

该项目有以下两个目标：（1）研究新算法，通过概率和统计性能测量给出最佳结构，（2）将获得的算法应用于与人类信号相对应的多模态信息源。第一年用于创建一类新的信息处理方法，得到了以下结果：（a）发现了一类新的期望最大化算法，该方法被命名为α-EM算法。传统的 log-EM 算法作为一种特殊的该工作的速度性能优于传统的 log-EM 方法。(b) 上述使用扩展对数的方法被发现适用于分离未知源信号。新方法被命名为α-ICA。去年，上述方法（a）和（b）被应用于多模态信息处理，获得的结果是（i）光流运动估计，（ii）活体人体估计。从功能性磁图像中观察大脑的活动发现，右半球后部有一个活动区域，这个活动区域是不对称的。正如上面所解释的，这个研究项目以许多可行的结果结束。

项目成果

Y.Matsuyama,et al.: "Fast Learning by the α-ECME Algorithm"Proc.ICONIP'99. 3. 1184-1190 (1999)

Y.Matsuyama 等人：“通过 α-ECME 算法进行快速学习”Proc.ICONIP99。3. 1184-1190 (1999)

The α-EM algorithm and its basic properties

α-EM算法及其基本性质

• 发表时间: 1999
• 期刊: Trans.IEICE J-82-D-I
• 作者: Y.Matsuyama;et al.;Y.Matsuyama
• 通讯作者: Y.Matsuyama

Multiple descent cost competitive learning and data-compressed 3-D morphing

• DOI: 10.1109/iconip.1999.844017
• 发表时间: 1999-11
• 期刊: ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378)
• 作者: Y. Matsuyama;T. Shimazu;G. Matsuo;T. Arisaka

The α-ICA algorithm

α-ICA 算法

• 发表时间: 2000
• 期刊: Proc.2nd Int.Workshop on ICA and BSS
• 作者: Y.Matsuyama;et al.
• 通讯作者: et al.

Y.Matsuyama: "The α-EM algorithm and its basic properties"Systems and Computers in Japan. 31. 12-23 (2000)

Y.Matsuyama：“α-EM 算法及其基本性质”《日本系统与计算机》31. 12-23 (2000)。