Inverse-Free Extreme Learning Machine With Optimal Information Updating
提出一种无需矩阵求逆的极限学习机，通过逐步增加隐藏节点并最优更新连接权重，降低了计算成本，理论证明训练误差单调递减且每步更新最优，实验验证了算法的有效性和准确性。
The extreme learning machine (ELM) has drawn insensitive research attentions due to its effectiveness in solving many machine learning problems. However, the matrix inversion operation involved in the algorithm is computational prohibitive and limits the wide applications of ELM in many scenarios. To overcome this problem, in this paper, we propose an inverse-free ELM to incrementally increase the number of hidden nodes, and update the connection weights progressively and optimally. Theoretical analysis proves the monotonic decrease of the training error with the proposed updating procedure and also proves the optimality in every updating step. Extensive numerical experiments show the effectiveness and accuracy of the proposed algorithm.