Multi-kernel fuzzy clustering based on auto-encoder for fMRI functional network

Abstract The existing clustering algorithms based on auto-encoder only use one layer of information. In this paper, we propose a new subspace clustering algorithm that uses multiple hidden layers of information from the stacked auto-encoder to construct different kernels. The proposed fuzzy multi-kernel clustering method based on auto-encoder is realized by updating the membership matrix and coefficients of these kernels until the value of objective function is iterated to the minimum error. Also, the proposed method combining auto-encoder achieves high dimension reduction of input data. In order to test the effectiveness of the algorithm, we first performed experiments on the published brain network dataset. Compared with MKFC, RMKKM and other algorithms, the proposed method can significantly improve accuracy. Specifically, the brain network has large dimensions when the functional magnetic resonance imaging (fMRI) data is preprocessed. Therefore, the method in this paper is applied to the Autism Spectrum Disorder (ASD) of the Autism Brain Imaging Data Exchange (ABIDE) database. The experiment results on the network dataset we constructed with high dimension are better than the present several clustering algorithms. The results show that the subspace information after dimensionality reduction is more conducive to clustering.