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通过交替最小化进行图形转换
Graph Transduction via Alternating Minimization |
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| 课程网址: |
http://videolectures.net/icml08_wang_gtvam/
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| 主讲教师: |
Jun Wang
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| 开课单位: |
哥伦比亚大学
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| 开课时间: |
2008-08-01
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| 课程语种: |
英语
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| 中文简介: |
图形转换方法通过学习分类函数来标记输入数据，该分类函数被规范化以在标记和未标记样本上沿着图表显示平滑度。实际上，这些算法对用户提供的初始标签集很敏感。例如，如果训练集包含弱标签，如果标签类之间存在不平衡或者未随机选择数据的标记部分，则分类准确度下降。本文介绍了一种传播算法，它可以更可靠地最小化图上函数和二进制标签矩阵的成本函数。成本函数概括了图形转换中的先前工作，并且还引入了用于标记不平衡的弹性的节点归一化术语。我们证明了函数的全局最小化是难以处理的，而是提供了交替最小化方案，该方案将函数和标签逐步调整到可靠的局部最小值。与现有方法不同，标签的最终传播不会过早地承诺错误标记并获得更一致的标签。显示了合成和实际分类任务的实验，包括数字和文本识别。与现有技术的半监督方法相比，实现了准确性的显着提高。当标记的实例有限时，优势更加显着。
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| 课程简介: |
Graph transduction methods label input data by learning a classification function that is regularized to exhibit smoothness along a graph over labeled and unlabeled samples. In practice, these algorithms are sensitive to the initial set of labels provided by the user. For instance, classification accuracy drops if the training set contains weak labels, if imbalances exist across label classes or if the labeled portion of the data is not chosen at random. This paper introduces a propagation algorithm that more reliably minimizes a cost function over both a function on the graph and a binary label matrix. The cost function generalizes prior work in graph transduction and also introduces node normalization terms for resilience to label imbalances. We demonstrate that global minimization of the function is intractable but instead provide an alternating minimization scheme that incrementally adjusts the function and the labels towards a reliable local minimum. Unlike prior methods, the resulting propagation of labels does not prematurely commit to an erroneous labeling and obtains more consistent labels. Experiments are shown for synthetic and real classification tasks including digit and text recognition. A substantial improvement in accuracy compared to state of the art semi-supervised methods is achieved. The advantage are even more dramatic when labeled instances are limited.
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| 关 键 词: |
图形转换方法; 分类函数; 标签集
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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: |
75
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