Accelerated Variational Dirichlet Process Mixtures
Accelerated Variational Dirichlet Process Mixtures
复制标题
DOI：
--
复制
发表时间：
2006-12
期刊：
影响因子：
--
通讯作者：
Kenichi Kurihara;M. Welling;N. Vlassis
中科院分区：
文献类型：
--
作者：
Kenichi Kurihara;M. Welling;N. Vlassis
狄利克雷过程（DP）混合模型是聚类应用的有前途的候选人，其中簇的数量是未知的先验。由于计算的考虑，这些模型不幸的是不适合大规模的数据挖掘应用。我们提出了一类确定性加速DP混合模型，可以常规地处理数百万的数据情况。加速是通过将kd-树合并到变分贝叶斯算法中来实现的，该算法用于在棒断裂表示中的DP混合物，类似于Blei和Jordan（2005）的算法。我们的算法在使用kd树和处理截断的方式上有所不同：我们只假设变分分布在一定水平后固定在它们的先验上。实验表明，相对于标准变分算法的加速比可以是显著的。
Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are unfortunately unsuitable for large scale data-mining applications. We propose a class of deterministic accelerated DP mixture models that can routinely handle millions of data-cases. The speedup is achieved by incorporating kd-trees into a variational Bayesian algorithm for DP mixtures in the stick-breaking representation, similar to that of Blei and Jordan (2005). Our algorithm differs in the use of kd-trees and in the way we handle truncation: we only assume that the variational distributions are fixed at their priors after a certain level. Experiments show that speedups relative to the standard variational algorithm can be significant.