Multi-layer Extreme Learning Machine Autoencoder With Subspace Structure Preserving

To deal with the clustering problem of high-dimensional complex data, it is usually reguired to reduce the dimensionality and then cluster, but the common dimensional reduction method does not consider the clustering characteristic of the data and the correlation between the samples, so it is difficult to ensure that the dimensional reduction method matches the clustering algorithm, which leads to the loss of clustering information. The nonlinear unsupervised dimensionality reduction method extreme learning machine autoencoder (ELM-AE) has been widely used in dimensionality reduction and denoising in recent years because of its fast learning speed and good generalization performance. In order to maintain the original subspace structure when high-dimensional data is projected into a low-dimensional space, the dimensional reduction method ML-SELM-AE is proposed. This method captures the deep features of the sample set by using the multi-layer extreme learning machine autoencoder while maintaining multi-subspace structure of clustered samples by self-representation model. Experimental results show that the method can effectively improve the clustering accuracy and achieve higher learning efficiency on UCI data, EEG data and gene expression data. Copyright ©2022 Acta Automatica Sinica. All rights reserved.