Multi View Spectral Clustering via ELM AE Ensemble Features Representations Learning
Spectral cluster based on multi view data has proven effective for clustering multi source real world data because consensus and complementary information of multi view data ensure the result of clustering. Feature learning is the vital step in spectral clustering, and excellent feature representations can effectively improve spectral clustering performance. In this article, we propose Multi View Spectral Clustering via ELM Autoencoder Ensemble Features Representations Learning (MvSC EF ELM) First, Extreme Learning Machine as an Autoencoder(ELM AE) learns feature representations by adopting a singular value decomposition, which reconstructs the inputs using the embedding feature space. Second, the single view and multi view spectral clustering algorithm are applied embedding feature representations space into the eigenspace and cluster's them, respectively. Experiments on benchmark datasets demonstrate that our approach empirically validates the power of ELM AE for feature learning from raw data and may effectively facilitate multi view spectral clustering and induce superior clustering results.
IEEE Access