Accelerated Independent Component Analysis Using Generalized Logarithm

使用广义对数加速独立分量分析

• 批准号: 13680465
• 负责人: MATSUYAMA Yasuo
• 金额: $ 2.24万
• 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: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2002
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13680465/
• 关键词: independent component analysis; convex divergence; momentum; functional magnetic resonance imaging; brain functional map; fast algorithm; f-ICA; fMRI; 脳機能解析

Independent Component Analysis (ICA) is a method to estimate unknown independent components which generate observed signals. In this research, the convex divergence was selected as the performance criterion for the independence. This measure is the source of the generalized logarithm. The obtained algorithm is named the f-ICA. The f-ICA contains the minimum mutual information ICA as a special case. The f-ICA can be realized as (a) the momentum method which adds the previous increment, and (b) the look-ahead method which adds the estimated future increment. Both methods show several times faster speed than the minimum mutual information method at the cost of a few additional memory. Thus, the first part of this project was successful by giving the accelerated ICA algorithm and novel properties of statistical measures related to the generalized logarithm.In addition to the theoretical sophistication, the following experimental results are successfully obtained in this project:(i) In any ICA algorithms, permutation indeterminacy is unavoidable. Users are obliged to check every independent component after the convergence of the algorithm. The investigator presented a way to inject prior knowledge as a regularization term. By this method, the most important component always appears as the first one.(ii) A software system was created, which is beyond a laboratory level, i.e., a more general user level.(iii) By using the above software system, human brain's functional maps are successfully obtained; (a) the main area of moving image recognition (dorsal occipital cortex), and (b) a separation of V1 and V2 regions of visual areas.

独立分量分析（伊卡）是一种估计产生观测信号的未知独立分量的方法。在这项研究中，凸散度被选为独立性的性能标准。这个测度是广义对数的来源。该算法被命名为f-ICA。f-ICA包含作为特殊情况的最小互信息伊卡。f-ICA可以实现为（a）增加先前增量的动量方法和（B）增加估计的未来增量的前瞻方法。这两种方法都比最小互信息方法快几倍，但代价是增加了一些内存。因此，本项目的第一部分是成功的，给出了加速伊卡算法和与广义对数相关的统计测度的新性质，除了理论上的复杂性之外，本项目还成功地获得了以下实验结果：（i）在任何伊卡算法中，排列不确定性是不可避免的。算法收敛后，用户必须检查每个独立分量。研究者提出了一种将先验知识作为正则化项注入的方法。通过这种方法，最重要的组件总是显示为第一个组件。(ii)创建了一个超出实验室水平的软件系统，即，更一般的用户级别。(iii)通过使用上述软件系统，成功地获得了人脑的功能图;（a）运动图像识别的主要区域（枕背皮层），和（B）视觉区域的V1和V2区域的分离。

项目成果

Y.Matsuyama, N.Katsumata, R.Kawamura: "Optimization transfer using convex divergence : f-ICA and alpha-EM algorithm with examples"Proc. Int. Symp. on Information Theory and Its Applications. 2. 667-670 (2002)

Y.Matsuyama、N.Katsumata、R.Kawamura：“使用凸散度的优化传输：f-ICA 和 alpha-EM 算法示例”Proc。

Y.Matsuyama, S.Imahara, N.Katsumata: "Optimization transfer for computational learning : A hierarchy from f-ICA and alpha-EM to their off springs"Proc. Int. Joint Conf. on Neural Networks. 3. 1883-1888 (2002)

Y.Matsuyama、S.Imahara、N.Katsumata：“计算学习的优化迁移：从 f-ICA 和 alpha-EM 到其后代的层次结构”Proc。

Y.Matsuyama, N.Katsumata, S.Imahara: "Independent component analysis using convex divergence"Proc. Int. Conf. on Neural Networks. 3. 1173-1178 (2001)

Y.Matsuyama、N.Katsumata、S.Imahara：“使用凸散度的独立成分分析”Proc。

Y. Matsuyama, N. Katsumata and R. Kawamura: "Optimization transfer using convex divergence: f-ICA and alpha-EM with examples"Proc. Int. Symp. on Information Theory and Its Applications. Vol. 2. 667-670 (2002)

Y. Matsuyama、N. Katsumata 和 R. Kawamura：“使用凸散度的优化传递：f-ICA 和 alpha-EM 示例”Proc。

Y.Matsuyama, N.Katsumata, R.Kawamura: "Optimization transfer using covex divergence : f-ICA and alpha-EM algorithin with examples"Proc. Int. Symp. on Information Theory and Its Applications. 2. 667-670 (2002)

Y.Matsuyama、N.Katsumata、R.Kawamura：“使用凸散度优化传输：f-ICA 和 alpha-EM 算法示例”Proc。