CO-CLUSTERING SEPARATELY EXCHANGEABLE NETWORK DATA
CO-CLUSTERING SEPARATELY EXCHANGEABLE NETWORK DATA
复制标题
DOI：
10.1214/13-aos1173
复制
发表时间：
2014-02-01
影响因子：
4.5
通讯作者：
Wolfe, Patrick J.
中科院分区：
文献类型：
--
作者：
Choi, David;Wolfe, Patrick J.
本文建立了随机块模型的性能，在解决划分一个二进制数组成子集的共聚类问题，假设只有数据是由一个非参数的过程，满足单独的交换条件。我们提供了对应于轮廓似然最大化和均方误差最小化的收敛速度为O-P（n（-1/4））的预言不等式，并表明在这种设置下，块模型可以被解释为生成非参数模型的最佳分段常数近似。我们还表明，大样本量的检测，在这样的数据中的合作集群表示具有很高的概率存在的合作集群的大小相等，渐近等价的连接在底层的生成过程。
This article establishes the performance of stochastic blockmodels in addressing the co-clustering problem of partitioning a binary array into subsets, assuming only that the data are generated by a nonparametric process satisfying the condition of separate exchangeability. We provide oracle inequalities with rate of convergence O-P(n(-1/4)) corresponding to profile likelihood maximization and mean-square error minimization, and show that the blockmodel can be interpreted in this setting as an optimal piecewise-constant approximation to the generative nonparametric model. We also show for large sample sizes that the detection of co-clusters in such data indicates with high probability the existence of co-clusters of equal size and asymptotically equivalent connectivity in the underlying generative process.