图优化的低秩双随机分解聚类 |
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引用本文: | 张涛,胡恩良,余景丽. 图优化的低秩双随机分解聚类[J]. 计算机应用研究, 2019, 36(2) |
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作者姓名: | 张涛 胡恩良 余景丽 |
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作者单位: | 云南师范大学 数学学院,昆明,650500;云南师范大学 数学学院,昆明,650500;云南师范大学 数学学院,昆明,650500 |
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基金项目: | 国家自然科学基金资助项目(61663049,61165012);云南师范大学研究生科研创新基金项目(yjs201678) |
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摘 要: | 低秩双随机矩阵分解聚类(low-rank doubly stochastic matrix decomposition for cluster analysis,DCD)是最近由Yang等人[16]提出的一种图聚类方法,它通过最小化KL(Kullback-Leibler)散度准则:KL(A,S),从图关联矩阵S中获得一个非负低秩双随机矩阵分解:A=UUT(U(0),并以U作为类标签矩阵进行聚类。在DCD方法中,因矩阵S是固定不可变的,故S初始取值选取的好坏对聚类结果有极大影响,这导致了它缺乏稳定性。针对这一问题,提出了一种基于图优化的DCD方法,将图关联矩阵S和DCD的优化集成在统一框架中,这改进和拓展了原始的DCD方法。实验结果表明,与DCD方法相比,图优化的DCD方法具有更好的聚类精确度和稳定性。
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关 键 词: | 低秩双随机矩阵分解 图优化 稳定性 聚类 |
收稿时间: | 2017-08-24 |
修稿时间: | 2019-01-07 |
Graph-optimized low-rank doubly stochastic decomposition for clustering |
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Affiliation: | Yunnan Normal University, |
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Abstract: | Clustering by DCD (low-rank doubly stochastic matrix decomposition) was recently proposed by Yang[16] as a method of graph clustering. DCD obtains a nonnegative low-rank doubly stochastic decomposition A=UUT(U(0) from the graph correlation matrix S by minimizing the criterion of KL (Kullback-Leibler) divergence: KL (A, S) , and clustering from U, as the class label matrix. In the method of DCD, because the S is pre-fixed, the initial value of S has a great influence on the clustering result, which leads to its lack of stability. Aiming at this problem, propose a DCD method based on graph optimization , and the optimization of graph correlation matrix S and DCD is integrated in a unified framework, which improves and extends the original DCD. The experimental results show that the graph-optimized DCD has better clustering accuracy and stability than the original DCD. |
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Keywords: | low-rank doubly stochastic matrix graph optimization stability clustering |
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