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MEG中函数连接的稀疏加低阶时间序列图形模型
Sparse plus low-rank graphical models of time series for functional connectivity in MEG |
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| 课程网址: |
https://videolectures.net/videos/kdd2016_nadkarni_functional_conn...
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| 主讲教师: |
Rahul Nadkarni
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| 开课单位: |
KDD 2016研讨会
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| 开课时间: |
2025-02-04
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| 课程语种: |
英语
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| 中文简介: |
由于其在各种应用领域的重要性，从高维观测中推断图形模型已成为机器学习和统计学中的一个重要问题。一个这样的应用是从神经成像数据（如脑磁图（MEG）记录）推断大脑区域之间的功能连接，这些数据产生具有良好时间和空间分辨率的信号。不幸的是，现有的学习应用于神经影像数据的图形模型的技术假设数据是随时间推移的i.i.d.，忽略了关键的时间动力学。此外，神经影像数据产生的信号并不是孤立存在的，因为大脑同时执行许多任务，因此大多数现有方法都可能引入虚假连接。我们通过引入一种在具有潜在过程的多个时间序列之间学习高斯图形模型的方法来解决这些问题。此外，我们通过使用分层惩罚来允许不同组MEG记录之间的异质性。所提出的方法被表述为凸优化问题，我们通过开发一种交替方向乘子算法有效地解决了这些问题。我们在综合数据、全球股指回报和真实的MEG数据集上评估了所提出的模型。
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| 课程简介: |
Inferring graphical models from high dimensional observations has become an important problem in machine learning and statistics because of its importance in a variety of application domains. One such application is inferring functional connectivity between brain regions from neuroimaging data such as magnetoencephalograpy (MEG) recordings that produce signals with good temporal and spatial resolution. Unfortunately, existing techniques to learn graphical models that have been applied to neuroimaging data have assumed the data to be i.i.d. over time, ignoring key temporal dynamics. Additionally, the signals that arise from neuroimaging data do not exist in isolation as the brain is performing many tasks simultaneously so that most existing methods can introduce spurious connections. We address these issues by introducing a method to learn Gaussian graphical models between multiple time series with latent processes. In addition, we allow for heterogeneity between different groups of MEG recordings by using a hierarchical penalty. The proposed methods are formulated as convex optimization problems that we efficiently solve by developing an alternating directions method of multipliers algorithm. We evaluate the proposed model on synthetic data as well as on global stock index returns and a real MEG data set.
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| 关 键 词: |
时间序列; 图形模型; 神经成像数据
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| 课程来源: |
视频讲座网
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| 数据采集: |
2025-04-06：liyq
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| 最后编审: |
2025-04-06：liyq
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| 阅读次数: |
13
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