  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.
