A Batch Inheritance Extreme Learning Machine Algorithm Based on Regular Optimization
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摘要:
极限学习机(ELM)作为一种新型神经网络，具有极快的训练速度和良好的泛化性能。针对极限学习机在处理高维数据时计算复杂度高，内存需求巨大的问题，该文提出一种批次继承极限学习机(B-ELM)算法。首先将数据集均分为不同批次，采用自动编码器网络对各批次数据进行降维处理；其次引入继承因子，建立相邻批次之间的关系，同时结合正则化框架构建拉格朗日优化函数，实现批次极限学习机数学建模；最后利用MNIST, NORB和CIFAR-10数据集进行测试实验。实验结果表明，所提算法具有较高的分类精度，并且有效降低了计算复杂度和内存消耗。
Abstract:As a new type of neural network, Extreme Learning Machine (ELM) has extremely fast training speed and good generalization performance. Considering the problem that the Extreme Learning Machine has high computational complexity and huge memory demand when dealing with high dimensional data, a Batch inheritance Extreme Learning Machine (B-ELM) algorithm is proposed. Firstly, the dataset is divided into different batches, and the automatic encoder network is used to reduce the dimension of each batch. Secondly, the inheritance factor is introduced to establish the relationship between adjacent batches. At the same time, the Lagrange optimization function is constructed by combining the regularization framework to realize the mathematical modeling of batch ELM. Finally, the MNIST, NORB and CIFAR-10 datasets are used for the test experiment. The experimental results show that the proposed algorithm not only has higher classification accuracy, but also reduces effectively computational complexity and memory consumption.
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表 1 不同数据集上的性能比较
分类方法 MNIST NORB CIFAR-10 精度(%) 训练时间(s) 精度(%) 训练时间(s) 精度(%) 训练时间(s) SAE 98.60 4042.36 86.28 6438.56 43.37 60514.26 SDA 98.72 3892.26 87.62 6572.14 43.61 87289.59 DBM 99.05 14505.14 89.65 18496.64 43.12 90123.53 ML-ELM 98.21 51.83 88.91 78.36 45.42 74.06 H-ELM 99.12 28.97 91.28 42.74 50.21 62.76 B-ELM 99.43 42.67 91.90 55.96 50.38 69.06 
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