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基于协作嵌入的高不完全数据多标签学习
Multi-Label Learning with Highly Incomplete Data via Collaborative Embedding |
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| 课程网址: |
http://videolectures.net/kdd2018_han_multi-label_embedding/
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| 主讲教师: |
Yufei Han
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| 开课单位: |
赛门铁克研究实验室
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| 开课时间: |
2018-11-23
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| 课程语种: |
英语
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| 中文简介: |
在不完整标签作业的情况下，我们致力于提高多标签学习的有效性。当前的大多数技术都假设数据实例的输入特征是完整的。然而，由于许多实际原因，包括数据收集不完整、注释器的标签不完整等，高度不完整的特征和弱标签分配的共存在现实世界的多标签学习应用程序中是一个具有挑战性和广泛感知的问题。当观察到的特征高度不完整时，现有的多标签学习算法不直接适用。在这项工作中，我们基于协作嵌入的思想，通过提出一种弱监督的多标签学习方法来解决这个问题。该方法提供了一个灵活的框架，通过在联合优化框架中耦合重建缺失特征和弱标签分配的过程，在转导和诱导模式下进行有效的多标签分类。它被设计为协作地恢复特征和标签信息，并提取特征简档和同一数据实例的多标签标签之间的预测关联。在公共基准数据集和真实安全事件数据上的大量实验证明，与其他最先进的算法相比，我们提出的方法可以提供更准确的转换和归纳分类。
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| 课程简介: |
Tremendous efforts have been dedicated to improving the effectiveness of multi-label learning with incomplete label assignments. Most of the current techniques assume that the input features of data instances are complete. Nevertheless, the co-occurrence of highly incomplete features and weak label assignments is a challenging and widely perceived issue in real-world multi-label learning applications due to a number of practical reasons including incomplete data collection, moderate labels from annotators, etc. Existing multi-label learning algorithms are not directly applicable when the observed features are highly incomplete. In this work, we attack this problem by proposing a weakly supervised multi-label learning approach, based on the idea of collaborative embedding. This approach provides a flexible framework to conduct efficient multi-label classification at both transductive and inductive mode by coupling the process of reconstructing missing features and weak label assignments in a joint optimisation framework. It is designed to collaboratively recover feature and label information, and extract the predictive association between the feature profile and the multi-label tag of the same data instance. Substantial experiments on public benchmark datasets and real security event data validate that our proposed method can provide distinctively more accurate transductive and inductive classification than other state-of-the-art algorithms.
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| 关 键 词: |
不完整标签作业; 提高多标签学习的有效性; 多标签学习应用程序; 学特征和弱标签分配
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| 课程来源: |
视频讲座网
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| 数据采集: |
2023-02-03：cyh
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| 最后编审: |
2023-02-03：cyh
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| 阅读次数: |
26
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图 书 馆