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基于自适应加权过程的多视图聚类
Multiview Clustering via Adaptively Weighted Procrustes |
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| 课程网址: |
http://videolectures.net/kdd2018_tian_multiview_clustering/
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| 主讲教师: |
Lai Tian
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| 开课单位: |
西北工业大学
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| 开课时间: |
2018-11-23
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| 课程语种: |
英语
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| 中文简介: |
在本文中，我们对单视图光谱聚类研究中提出的光谱旋转技术进行了多视图扩展。由于光谱旋转与点匹配的Procrustes分析密切相关，我们指出经典的ProcrustesAverage方法可以用于多视图聚类。此外，我们表明，在多视图任务中直接应用Procrustes Average（PA）在理论和经验上可能不是最优的，因为它没有考虑不同视图的聚类能力差异。除此之外，我们提出了一种自适应加权过程（AWP）方法来克服上述缺陷。我们的新AWP将视图与其聚类能力进行加权，并相应地形成加权的Procrustes Average问题。求解新模型的优化算法分析了计算复杂性，并保证了收敛性。在五个真实世界数据集上的实验证明了新模型的有效性和效率。
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| 课程简介: |
In this paper, we make a multiview extension of the spectral rotation technique raised in single view spectral clustering research. Since spectral rotation is closely related to the Procrustes Analysis for points matching, we point out that classical Procrustes Average approach can be used for multiview clustering. Besides, we show that direct applying Procrustes Average (PA) in multiview tasks may not be optimal theoretically and empirically, since it does not take the clustering capacity differences of different views into consideration. Other than that, we propose an Adaptively Weighted Procrustes (AWP) approach to overcome the aforementioned deficiency. Our new AWP weights views with their clustering capacities and forms a weighted Procrustes Average problem accordingly. The optimization algorithm to solve the new model is computational complexity analyzed and convergence guaranteed. Experiments on five real-world datasets demonstrate the effectiveness and efficiency of the new models.
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| 关 键 词: |
光谱旋转与点匹配; ProcrustesAverage方法; 自适应加权过程; 真实世界数据集
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| 课程来源: |
视频讲座网
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| 数据采集: |
2023-01-24：cyh
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| 最后编审: |
2023-01-24：cyh
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| 阅读次数: |
46
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