Abstract Aiming at the problems of high memory energy consumption,low classification accuracy and poor generalization when dealing with high-dimensional data, a batch hierarchical coding extreme learning machine algorithm is proposed.Firstly,the dataset is processed in batches to reduce the data dimension and reduce input complexity.Then,the multi-layer automatic encoder structure is used to unsupervise the batch data to achieve deep feature extraction.Finally,the manifold regularization is used to construct a manifold classifier with inheritance factors to maintain data integrity and improve the generalization performance of the algorithm.The experimental results show that the method is simple to implement,and the classification accuracy on the NORB,MNIST,and USPS datasets can reach 92.16%,99.35%,and 98.86%,respectively.Compared with other ELM algorithms,it has obvious advantages in reducing computational complexity and reducing CPU memory consumption.
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2021, Vol. 42 
