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使用任务聚类进行多任务学习，并应用于植物品种的预测建模和GWAS
Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties |
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| 课程网址: |
http://videolectures.net/kdd2017_yu_predictive_modeling/
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| 主讲教师: |
Ming Yu
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| 开课单位: |
IBM Thomas J. Watson 研究中心
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| 开课时间: |
2017-12-01
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| 课程语种: |
英语
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| 中文简介: |
在多个输入和多个输出变量或任务之间推断预测图在数据科学中有无数的应用。多任务学习尝试同时学习到多个输出任务的映射，并在它们之间共享信息。我们为稀疏线性回归提出了一种新颖的多任务学习框架，其中从数据中自动推断出完整的任务层次结构，并假设任务参数遵循分层树结构。树的叶子是单个任务的参数，根是近似所有任务的全局模型。我们应用所提出的方法来开发和评估：（a）使用大规模和自动遥感数据的植物性状预测模型，以及（b）GWAS 方法映射此类衍生表型以代替手工测量的性状。与其他方法相比，我们展示了我们的方法的卓越性能，以及发现任务之间的分层分组的有用性。我们的研究结果表明，确实可以从遥感数据中获得更丰富的遗传图谱。此外，我们发现的分组从植物科学的角度揭示了有趣的见解。
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| 课程简介: |
Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science. Multi-task learning attempts to learn the maps to several output tasks simultaneously with information sharing between them. We propose a novel multi-task learning framework for sparse linear regression, where a full task hierarchy is automatically inferred from the data, with the assumption that the task parameters follow a hierarchical tree structure. The leaves of the tree are the parameters for individual tasks, and the root is the global model that approximates all the tasks. We apply the proposed approach to develop and evaluate: (a) predictive models of plant traits using large-scale and automated remote sensing data, and (b) GWAS methodologies mapping such derived phenotypes in lieu of hand-measured traits. We demonstrate the superior performance of our approach compared to other methods, as well as the usefulness of discovering hierarchical groupings between tasks. Our results suggest that richer genetic mapping can indeed be obtained from the remote sensing data. In addition, our discovered groupings reveal interesting insights from a plant science perspective.
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| 关 键 词: |
数据科学; 建模应用; 模型预测
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| 课程来源: |
视频讲座网
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| 数据采集: |
2022-03-20：hqh
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| 最后编审: |
2022-03-20：hqh
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| 阅读次数: |
50
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