Extreme learning machine (ELM) as well as its variants have been widely used in many fields for its good generalization performance and fast learning speed. Though distributed ELM can sufficiently process large-scale labeled training data, the current technology is not able to process partial labeled or unlabeled training data. Therefore, we propose a new unified distributed ELM with supervised, semi-supervised and unsupervised learning based on MapReduce framework, called U-DELM. The U-DELM method can be used to overcome the existing distributed ELM framework’s lack of ability to process partially labeled and unlabeled training data. We first compare the computation formulas of supervised, semi-supervised and unsupervised learning methods and found that the majority of expensive computations are decomposable. Next, MapReduce framework based U-DELM is proposed, which extracts three different matrices continued multiplications from the three computational formulas introduced above. After that, we transform the cumulative sums respectively to make them suitable for MapReduce. Then, the combination of the three computational formulas are used to solve the output weight in three different learning methods. Finally, by using benchmark and synthetic datasets, we are able to test and verify the efficiency and effectiveness of U-DELM on learning massive data. Results prove that U-DELM can achieve unified distribution on supervised, semi-supervised and unsupervised learning.

中文翻译：

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具有监督，半监督和无监督大数据学习的统一分布式ELM框架

极限学习机（ELM）及其变体以其良好的泛化性能和快速的学习速度已在许多领域中得到广泛使用。尽管分布式ELM可以充分处理大规模的带标签的训练数据，但是当前技术无法处理部分带标签的或未标记的训练数据。因此，我们提出了一种基于MapReduce框架的具有监督，半监督和无监督学习的新的统一分布式ELM，称为U-DELM。U-DELM方法可用于克服现有的分布式ELM框架缺乏处理部分标记和未标记的训练数据的能力。我们首先比较了有监督，半监督和无监督学习方法的计算公式，发现大多数昂贵的计算都是可分解的。下一个，提出了一种基于MapReduce框架的U-DELM，它从上面介绍的三个计算公式中提取了三个不同的矩阵连续乘法。之后，我们分别转换累积和以使其适合MapReduce。然后，将三种计算公式的组合用于求解三种不同学习方法中的输出权重。最后，通过使用基准数据集和综合数据集，我们能够测试和验证U-DELM在学习海量数据上的效率和有效性。结果证明，U-DELM可以在有监督，半监督和无监督学习上实现统一分布。我们分别转换累计和以使其适合MapReduce。然后，将三种计算公式的组合用于求解三种不同学习方法中的输出权重。最后，通过使用基准数据集和综合数据集，我们能够测试和验证U-DELM在学习海量数据上的效率和有效性。结果证明，U-DELM可以在有监督，半监督和无监督学习上实现统一分布。我们分别转换累积和以使其适合MapReduce。然后，将三种计算公式的组合用于求解三种不同学习方法中的输出权重。最后，通过使用基准数据集和综合数据集，我们能够测试和验证U-DELM在学习海量数据上的效率和有效性。结果证明，U-DELM可以在有监督，半监督和无监督学习上实现统一分布。