Studies on Multimodal Information Processing Based Upon Fast Expectation-Maximization

该项目有以下两个目标：（1）研究新的算法，这些算法可以给出由概率和统计性能衡量的最佳结构;（2）将所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。（a）提出了一类新的期望最大化算法，称为α-EM算法。α-EM算法包含了传统的log-EM算法作为特例。在速度上优于传统的对数EM方法。这项工作获得了电信进步基金会奖。(b)上述方法使用扩展对数被发现适用于分离未知源信号的独立分量分析。去年，上述方法（a）和（B）被应用于多模态信息处理。得到的结果是（i）运动估计的光流，（ii）估计活的人脑活动的功能磁图像。结果发现，在右半球后部有一个活动区。如上所述，本研究项目以大量可行的结果结束。

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.