摘要
为避免传统谱聚类算法高复杂度的应用局限,基于地标表示的谱聚类算法利用地标点与数据集各点间的相似度矩阵,有效降低了谱嵌入的计算复杂度。在大数据集情况下,现有的随机抽取地标点的方法会影响聚类结果的稳定性,k均值中心点方法面临收敛时间未知、反复读取数据的问题。该文将近似奇异值分解应用于基于地标点的谱聚类,设计了一种快速地标点采样算法。该算法利用由近似奇异向量矩阵行向量的长度计算的抽样概率来进行抽样,同随机抽样策略相比,保证了聚类结果的稳定性和精度,同k均值中心点策略相比降低了算法复杂度。同时从理论上分析了抽样结果对原始数据的信息保持性,并对算法的性能进行了实验验证。
The applicability of traditional spectral clustering is limited by its high complexity in large-scale data sets. Through construction of affinity matrix between landmark points and data points, the Landmark-based Spectral Clustering （LSC） algorithm can significantly reduce the computational complexity of spectral embedding. It is vital for clustering results to apply the suitable strategies of the generation of landmark points. While considering big data problems, the existing generation strategies of landmark points face some deficiencies： the unstable results of random sampling, along with the unknown convergence time and the repeatability of data reading in k-means centers method. In this paper, a rapid landmark-sampling spectral clustering algorithm based on the approximate singular value decomposition is designed, which makes the sampling probability of each landmark point decided by the row norm of the approximate singular vector matrix. Compared with LSC algorithm based on random sampling, the clustering result of new algorithm is more stable and accurate; compared with LSC algorithm based on k-means centers, the new algorithm reduces the computational complexity. Moreover, the preservation of information in original data is analyzed for the landmark-sampling results theoretically. At the same time, the performance of new approach is verified by the experiments in some public data sets.
作者
叶茂
刘文芬
YE Mao LIU Wenfen(PLA Information Engineering University, Zhengzhou 450002, China State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002, China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2017年第2期278-284,共7页
Journal of Electronics & Information Technology
基金
国家973计划(2012CB315905)
国家自然科学基金(61502527
61379150)~~
关键词
地标点采样
大数据
谱聚类
近似奇异值分解
Landmark sampling
Big data
Spectral clustering
Approximate singular value decomposition
