面向大数据的大规模并行特征选择
Massively-Parallel Feature Selection for Big Data
摘要：
我们提出了并行，向前向后修剪（PFBP）算法的特征选择（FS）在大数据设置（高维度和/或样本大小）。为了应对大数据的挑战，FS PFBP根据行（样本、训练样本）和列（特征）对数据矩阵进行分区。通过采用$p$ -条件独立性测试和元分析技术的值的概念，PFBP管理，只依赖于本地的计算分区，同时最大限度地减少通信成本。然后，它采用强大而安全的（渐近合理的）算法来做出早期的近似决策，例如在随后的迭代中提前放弃考虑功能，在同一迭代中提前停止考虑功能，或者在每次迭代中提前返回获胜者。PFBP提供了一个因果网络（贝叶斯网络或最大祖先图）忠实地表示数据分布的最优性的渐近保证。我们的实证分析证实了一个超线性加速的算法，增加样本大小，线性可扩展性的功能和处理核心的数量，同时主导其他竞争对手的同类算法。
英文摘要：
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as columns (features). By employing the concepts of $p$-values of conditional independence tests and meta-analysis techniques PFBP manages to rely only on computations local to a partition while minimizing communication costs. Then, it employs powerful and safe (asymptotically sound) heuristics to make early, approximate decisions, such as Early Dropping of features from consideration in subsequent iterations, Early Stopping of consideration of features within the same iteration, or Early Return of the winner in each iteration. PFBP provides asymptotic guarantees of optimality for data distributions faithfully representable by a causal network (Bayesian network or maximal ancestral graph). Our empirical analysis confirms a super-linear speedup of the algorithm with increasing sample size, linear scalability with respect to the number of features and processing cores, while dominating other competitive algorithms in its class.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
