图 书 馆
具有双重异质性的学习：一个非参数Bayes模型Learning with Dual Heterogeneity: A Nonparametric Bayes Model |
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| 课程网址: | http://videolectures.net/kdd2014_yang_dual_heterogeneity/ |
| 主讲教师: | Hongxia Yang |
| 开课单位: | IBM托马斯沃森研究中心 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  传统的数据挖掘技术旨在对单一类型的异构进行建模，例如多任务学习建模任务异构、多视图学习建模视图异构等。最近出现了各种实际应用，它们表现出双重异构，即任务异质性和视图异质性。示例包括跨多个组织的内部威胁检测、不同域中的 Web 图像分类等。 解决此类问题的现有方法通常假设多个任务具有同等相关性且多个视图具有同等一致性，这限制了它们在具有不同任务相关性的复杂环境中的应用并查看一致性。在本文中，我们通过非参数贝叶斯模型对任务相关性和视图一致性进行自适应建模来推进最先进的技术：我们使用具有稀疏协方差的正态惩罚对任务相关性进行建模，并使用矩阵 Dirichlet 过程对视图一致性进行建模。基于此模型，我们提出了使用高效 Gibbs 采样器的 NOBLE 算法。在多个真实数据集上的实验结果证明了该算法的有效性。 |
| 课程简介: | Traditional data mining techniques are designed to model a single type of heterogeneity, such as multi-task learning for modeling task heterogeneity, multi-view learning for modeling view heterogeneity, etc. Recently, a variety of real applications emerged, which exhibit dual heterogeneity, namely both task heterogeneity and view heterogeneity. Examples include insider threat detection across multiple organizations, web image classification in different domains, etc. Existing methods for addressing such problems typically assume that multiple tasks are equally related and multiple views are equally consistent, which limits their application in complex settings with varying task relatedness and view consistency. In this paper, we advance state-of-the-art techniques by adaptively modeling task relatedness and view consistency via a nonparametric Bayes model: we model task relatedness using normal penalty with sparse covariances, and view consistency using matrix Dirichlet process. Based on this model, we propose the NOBLE algorithm using an efficient Gibbs sampler. Experimental results on multiple real data sets demonstrate the effectiveness of the proposed algorithm. |
| 关 键 词: | Web 图像分类; 数据挖掘; 贝叶斯模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-09：zyk |
| 最后编审: | 2021-06-09：zyk |
| 阅读次数: | 38 |