Conventional Extreme Learning Machines utilize Moore–Penrose generalized pseudo-inverse to solve hidden layer activation matrix and perform analytical determination of output weights. Scalability is the major concern to be addressed in Extreme Learning Machines while dealing with large dataset. Motivated by these scalability concerns, this paper proposes a novel tensor decomposition based Extreme Learning Machine which utilize PARAFAC and TUCKER decomposition based techniques in a SPARK platform. This proposed Extreme Learning Machine achieve reduced training time and better accuracy when compared with a conventional Extreme Learning Machine.

中文翻译：

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基于张量分解的极限学习机训练方法

常规的极限学习机利用Moore-Penrose广义伪逆来求解隐藏层激活矩阵并进行输出权重的分析确定。可扩展性是极端学习机在处理大型数据集时要解决的主要问题。出于这些可扩展性的考虑，本文提出了一种基于张量分解的极限学习机，该机在SPARK平台中利用了基于PARAFAC和TUCKER分解的技术。与传统的极限学习机相比，该提议的极限学习机可减少训练时间并提高准确性。