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L1-Ball上的高效投影用于高维学习
Efficient Projections onto the L1-Ball for Learning in High Dimensions |
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| 课程网址: |
http://videolectures.net/icml08_singer_ep/
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| 主讲教师: |
Yoram Singer
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| 开课单位: |
耶路撒冷希伯来大学
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| 开课时间: |
2008-08-29
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| 课程语种: |
英语
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| 中文简介: |
我们描述了将矢量投影到L1球上的有效算法。我们提出了两种投影方法。第一个在O（n）时间内执行精确投影，其中n是空间的维度。第二个对矢量k起作用，其元素在L1球外被扰动，以O（k log（n））时间投影。此设置对于稀疏要素空间（如文本分类应用程序）中的在线学习特别有用。我们在众多批量和在线学习任务中展示了算法的优点和有效性。我们表明随着我们有效的投影程序增加的随机梯度投影方法的变体优于现有的优化技术，如内点法。我们还表明，在线设置中，L1投影的梯度更新优于EG算法，同时获得具有高度稀疏度的模型。** //免责声明：// VideoLectures.NET强调此话题未在其完整持续时间内记录但由于其内容价值已发布。**
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| 课程简介: |
We describe efficient algorithms for projecting a vector onto the L1-ball. We present two methods for projection. The first performs exact projection in O(n) time, where n is the dimension of the space. The second works on vectors k of whose elements are perturbed outside the L1-ball, projecting in O(k log(n)) time. This setting is especially useful for online learning in sparse feature spaces such as text categorization applications. We demonstrate the merits and effectiveness of our algorithms in numerous batch and online learning tasks. We show that variants of stochastic gradient projection methods augmented with our efficient projection procedures outperform state-of-the-art optimization techniques such as interior point methods. We also show that in online settings gradient updates with L1 projections outperform the EG algorithm while obtaining models with high degrees of sparsity. **//Disclaimer:// VideoLectures.NET emphasizes that this talk was not recorded in its full duration but due to its content value was published.**
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| 关 键 词: |
矢量投影; 稀疏要素空间; 在线学习
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-21：lxf
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| 阅读次数: |
72
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