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非负共享子空间学习及其在社会化媒体检索中的应用
Nonnegative Shared Subspace Learning and Its Application to Social Media Retrieval |
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| 课程网址: |
http://videolectures.net/kdd2010_gupta_nssli/
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| 主讲教师: |
Sunil Kumar Gupta
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| 开课单位: |
科廷科技大学
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| 开课时间: |
2010-10-01
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| 课程语种: |
英语
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| 中文简介: |
尽管标签在在线图像和视频共享系统中越来越流行, 但众所周知, 标签是嘈杂、模糊、不完整和主观的。这些因素会严重影响基于社交标签的 web 检索系统的精度。因此, 提高这些基于社交标签的网络检索系统的精度性能已成为一个越来越重要的研究课题。为此, 我们提出了一个共享子空间学习框架, 以利用辅助源来提高从主数据集检索的性能。这是通过在一个联合非负矩阵分解下学习两个源之间的共享子空间来实现的, 在这个组合下, 子空间共享的水平可以显式控制。我们推导出一种有效的因子分解算法, 分析其复杂性, 并提供收敛性的证明。我们验证了图像和视频检索任务的框架, 其中 labelme 数据集中的标记用于提高 flickr 数据集的图像检索性能和 youtube 数据集的视频检索性能。这对如何利用和转让现有辅助标记资源中的知识以改进另一个社交网络检索系统具有重要意义。我们的共享子空间学习框架适用于需要利用多个异构数据集之间的优势的一系列问题。
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| 课程简介: |
Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset. This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.
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| 关 键 词: |
计算机科学; 信息检索; 空间学习和社交媒体检索中的应用
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-03：毛岱琦(课程编辑志愿者)
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| 阅读次数: |
33
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