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无监督的功能磁共振成像分析
Unsupervised fMRI Analysis |
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| 课程网址: |
http://videolectures.net/fmri06_hardoon_ufa/
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| 主讲教师: |
David R. Hardoon
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| 开课单位: |
伦敦全球大学
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| 开课时间: |
2007-02-25
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| 课程语种: |
英语
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| 中文简介: |
最近已经使用机器学习方法来分析刺激类别和fMRI反应之间的关系[2,14,15,11,13,8,9,1,12,7]。在这里，我们引入了一种新的无监督机器学习方法来进行fMRI分析方法，其中刺激类型的简单分类描述（例如任务类型）被更具信息性的刺激特征向量所取代。我们使用刺激类型的分类描述将这种新方法与fMRI数据的标准支持向量机（SVM）分析进行了比较。以下研究与传统的无监督方法的不同之处在于我们利用了刺激特征。我们使用核心典型相关分析（KCCA）来学习fMRI体积与在特定时间点呈现的相应刺激特征之间的相关性。 CCA可以被视为找到两组变量的基矢量的问题，使得变量的投影与这些基矢量的相关性相互最大化。在新特征空间中执行CCA之前，KCCA首先将数据投影到更高维度的特征空间。
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| 课程简介: |
Recently machine learning methodology has been used increasing to analyze the relationship between stimulus categories and fMRI responses [2, 14, 15, 11, 13, 8, 9, 1, 12, 7]. Here, we introduce a new unsupervised machine learning approach to fMRI analysis approach, in which the simple categorical description of stimulus type (e.g. type of task) is replaced by a more informative vector of stimulus features. We compared this new approach with a standard Support Vector Machine (SVM) analysis of fMRI data using a categorical description of stimulus type. The following study differs from conventional unsupervised approaches in that we make use of the stimulus characteristics. We use kernel Canonical Correlation Analysis (KCCA) to learn the correlation between the fMRI volume and the corresponding stimulus features presented at a particular time point. CCA can be seen as the problem of finding basis vectors for two sets of variables such that the correlation of the projections of the variables onto these basis vectors are mutually maximised. KCCA first projects the data into a higher dimensional feature space before performing CCA in the new feature space.
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| 关 键 词: |
无监督的机器学习功能磁共振成像分析方法; 刺激特征向量; fMRI数据分析
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-09-28：heyf
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| 阅读次数: |
47
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