Asynchronous Feature Extraction for Large-Scale Linear Predictors
Asynchronous Feature Extraction for Large-Scale Linear Predictors
复制标题
大规模线性预测器的异步特征提取
DOI：
10.1007/978-3-319-46128-1_38
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Shin Matsushima
中科院分区：
文献类型：
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作者：
Masao Yamagishi;Masahiro Yukawa;Isao Yamada;Katsuhide Fujita;Shin Matsushima
正在深入研究从具有大量可能特征的数据集中学习以获得更准确的预测因子。在本文中，我们的目标是通过使用L1正则化风险最小化问题的时间和空间的计算资源进行有效的学习。这是通过从大量不必要的特征中集中于有效特征来实现的。为了实现这一目标，我们提出了一种多线程方案，该方案同时运行用于在主内存中开发看似重要的功能和仅更新有关重要功能的参数的进程。我们通过计算实验验证了我们的方法，表明我们提出的方案可以处理TB级的优化问题与一台机器。
Learning from datasets with a massive number of possible features to obtain more accurate predictors is being intensively studied. In this paper, we aim to perform effective learning by using the L1 regularized risk minimization problems regarding both time and space computational resources. This is accomplished by concentrating on the effective features from among a large number of unnecessary features. To achieve this, we propose a multithreaded scheme that simultaneously runs processes for developing seemingly important features in the main memory and updating parameters regarding only the important features. We verified our method through computational experiments, showing that our proposed scheme can handle terabyte-scale optimization problems with one machine.