Learning Common Features from fMRI Data of Multiple Subjects-猫眼课题宝

从多个受试者的功能磁共振成像数据中学习共同特征

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2003

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J. Ramish;Tom Michael Mitchell

功能性磁共振成像（fMRI）是一种大脑成像技术，它使心理学家能够确定大脑的哪些部分参与了各种任务。最近，Mitchell等人（2003）以一种新颖的方式使用了fMRI：利用机器学习算法从中推断一个人的精神状态。Wang， Hutchinson和Mitchell（2003）已经扩展了这些算法，使用硬编码的公共表示来进行跨主题的预测。我们进一步发展了跨学科聚类，这是一种学习共同表征的方法。这种方法不仅提供了学习的理论优势，而且似乎也提供了改进的跨学科预测的经验优势。然而，实证研究仅限于单个数据集（句子-图像），因此需要进一步的工作来确认其一般效用。我们的其他几个实验表明，无监督学习通常与有监督学习的准确率几乎一样高，在某些情况下甚至更高。最后，我们简要地列出了其他几种不太成功的方法来解决跨学科预测问题。大纲

Functional Magnetic Resonance Imaging (fMRI), a brain imaging technique, has allowed psychologists to identify what parts of the brain are involved in various tasks. Recently, Mitchell et al (2003) have used fMRI in a novel way: to infer from it a person’s mental states using machine learning algorithms. Wang, Hutchinson, and Mitchell (2003) have extended these algorithms to make predictions across subjects, using hardcoded common representations. We have gone further to develop cross-subject clustering, a method of learning common representations. This method not only offers the theoretical advantage of learning, but also appears to offer the empirical advantage of improved cross-subject predictions. The empirical studies were limited to a single dataset (Sentence-then-Picture), however, so further work is needed to confirm its general utility. Several of our other experiments demonstrate that unsupervised learning generally attains accuracies nearly as high as those of supervised learning across subjects, and in some cases higher. Finally, we briefly catalog several other less successful approaches to the cross-subject prediction problem. Outline

Learning Common Features from fMRI Data of Multiple Subjects

从多个受试者的功能磁共振成像数据中学习共同特征

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