Our proposal does not require any pretraining and includes the three following steps: generating various deep embeddings from the original data, constructing a sparse and low-dimensional ensemble affinity matrix based on anchors strategy and applying spectral clustering to obtain the common space shared by multiple deep representations. To alleviate the impact of hyperparameters setting, we propose a model which combines spectral clustering and deep autoencoder strengths in an ensemble framework. These strategies generally improve clustering performance, however deep autoencoder setting issues impede the robustness of these approaches. ![]() Several works have studied clustering strategies that combine classical clustering algorithms and deep learning methods.
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