Extreme Learning Machine（ELM） is an effective classification learning algorithm which has the advantages of fast learning speed, high generalization performance, and good approximation ability. With the development of unsupervised learning, integrating ELM with autoencoders has become a new perspective for extracting features using unlabeled data sets, for example, Extreme Learning Machine Autoencoder（ELM-AE）, which is an unsupervised neural network. The main components representing the original sample and its learning process can be found without iteration. It reconstructs the input signal to obtain the main characteristics of the original sample, and considers the global information of the original data to avoid the loss of information. However, this type of method does not consider the inherent Manifold structure of the data, that is, the neighbor structure relationship between the samples. This paper draws on the idea of extreme learning machine autoencoder, and proposes a Manifold-based Extreme Learning Machine autoencoder algorithm （M-ELM）. The algorithm is a nonlinear unsupervised feature extraction method, which combines manifold learning to maintain local information of the data, and simultaneously learns the similarity matrix during feature extraction instead of using a specific formula to calculate the similarity between samples. By conducting experiments on IRIS data set, EEG data set and gene expression data set, the accuracy of this algorithm and other unsupervised learning methods including PCA, LPP, NPE, LE and ELM-AE algorithm after [k]-means are compared, to show the effectiveness of this algorithm.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2006-0007