论文标题
大型数据集的极限学习机中基于等级的伪内计算
Rank Based Pseudoinverse Computation in Extreme Learning Machine for Large Datasets
论文作者
论文摘要
Extreme Learning Machine（ELM）是一种基于单个隐藏层馈送前馈神经网络（SLFN）的分类问题，回归问题的有效和有效的学习算法。在文献中已经显示，它具有更快的收敛性和良好的中等数据集能力。但是，当有大量隐藏节点或大量实例来训练复杂的模式识别问题时，计算伪verse的挑战涉及。为了解决这个问题，文献中提出了一些诸如EM-ELM之类的方法。在本文中，引入了隐藏层矩阵的新基于等级的矩阵分解，以具有最佳的训练时间，并降低了隐藏层中大量隐藏节点的计算复杂性。结果表明，它具有持续的训练时间，它更接近最小的训练时间，并且与DF-ELM算法的最差训练时间相去甚远，而DF-ELM算法在最近的文献中已显示出有效的效率。
Extreme Learning Machine (ELM) is an efficient and effective least-square-based learning algorithm for classification, regression problems based on single hidden layer feed-forward neural network (SLFN). It has been shown in the literature that it has faster convergence and good generalization ability for moderate datasets. But, there is great deal of challenge involved in computing the pseudoinverse when there are large numbers of hidden nodes or for large number of instances to train complex pattern recognition problems. To address this problem, a few approaches such as EM-ELM, DF-ELM have been proposed in the literature. In this paper, a new rank-based matrix decomposition of the hidden layer matrix is introduced to have the optimal training time and reduce the computational complexity for a large number of hidden nodes in the hidden layer. The results show that it has constant training time which is closer towards the minimal training time and very far from worst-case training time of the DF-ELM algorithm that has been shown efficient in the recent literature.
