Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)
This work addresses robustness issues in deep clustering for researchers and practitioners, though it is incremental as it builds on existing spectral and deep learning approaches.
The paper tackles the problem of robustness in deep clustering methods by proposing a new model that combines spectral clustering and deep autoencoders in an ensemble learning framework, demonstrating improved performance and robustness on benchmark datasets compared to state-of-the-art methods.
Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way, where deep representation and clusters are learned jointly by optimizing a single objective function. Both strategies improve clustering performance, however the robustness of these approaches is impeded by several deep autoencoder setting issues, among which the weights initialization, the width and number of layers or the number of epochs. To alleviate the impact of such hyperparameters setting on the clustering performance, we propose a new model which combines the spectral clustering and deep autoencoder strengths in an ensemble learning framework. Extensive experiments on various benchmark datasets demonstrate the potential and robustness of our approach compared to state-of-the-art deep clustering methods.