With the explosion of the size of digital dataset, the limiting factor for
decomposition algorithms is the \emph{number of passes} over the input, as the
input is often stored out-of-core or even off-site. Moreover, we're only
interested in algorithms that operate in \emph{constant memory} w.r.t. to the
input size, so that arbitrarily large input can be processed. In this paper, we
present a practical comparison of two such algorithms: a distributed method
that operates in a single pass over the input vs. a streamed two-pass
stochastic algorithm. The experiments track the effect of distributed
computing, oversampling and memory trade-offs on the accuracy and performance
of the two algorithms. To ensure meaningful results, we choose the input to be
a real dataset, namely the whole of the English Wikipedia, in the application
settings of Latent Semantic Analysis.

本文研究了常量内存和具有单个传递特性的分布式算法与两个阶段随机算法之间的比较，以及分布式计算，过采样和内存权衡对这两种算法的准确性和性能的影响，使用的实际数据是英文维基百科的全部内容，在 Latent Semantic Analysis 应用中。