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近似保证聚类混合布雷格曼
Mixed Bregman Clustering with Approximation Guarantees |
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| 课程网址: |
http://videolectures.net/ecmlpkdd08_kivinen_mbcw/
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| 主讲教师: |
Jyrki Kivinen; Richard Knock; Panu Luosto
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| 开课单位: |
澳大利亚国立大学
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| 开课时间: |
2008-09-15
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| 课程语种: |
英语
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| 中文简介: |
最近的两个突破极大地提高了k均值聚类的范围和性能：初始化步骤的平方欧几里得播种，迭代步骤的Bregman聚类。在本文中，我们首先将这两个框架统一起来，将前一个改进推广到Bregman Seeding，这是一种使用Bregman分歧的有偏随机播种技术，同时也推广了其重要的理论近似保证。最后，我们得到了一个完整的Bregman硬聚类算法，在初始化和迭代步骤中整合了手头的失真。我们的第二个贡献是进一步推广该算法来处理混合的布里格曼畸变，从而消除布里格曼发散的不对称性。与其他对称化方法相比，我们的方法保持了算法的简单性，并且允许我们从规则的Bregman聚类中归纳出理论上的保证。
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| 课程简介: |
Two recent breakthroughs have dramatically improved the scope and performance of k-means clustering: squared Euclidean seeding for the initialization step, and Bregman clustering for the iterative step. In this paper, we first unite the two frameworks by generalizing the former improvement to Bregman seeding - a biased randomized seeding technique using Bregman divergences - while generalizing its important theoretical approximation guarantees as well. We end up with a complete Bregman hard clustering algorithm integrating the distortion at hand in both the initialization and iterative steps. Our second contribution is to further generalize this algorithm to handle mixed Bregman distortions, which smooth out the asymetricity of Bregman divergences. In contrast to some other symmetrization approaches, our approach keeps the algorithm simple and allows us to generalize theoretical guarantees from regular Bregman clustering.
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| 关 键 词: |
聚类; 计算机科学; 算法
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-12-06：lxf
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| 阅读次数: |
40
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