Deep Spectral Clustering using Dual Autoencoder Network
This work addresses the challenge of achieving more effective clustering in learning and vision domains, representing an incremental improvement over existing deep clustering methods.
The paper tackled the problem of improving clustering performance by proposing a joint learning framework that integrates discriminative embedding with spectral clustering, using a dual autoencoder network for robust latent representations and mutual information estimation for enhanced discriminative information. Experimental results on benchmark datasets show that the method significantly outperforms state-of-the-art clustering approaches.
The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering. We first devise a dual autoencoder network, which enforces the reconstruction constraint for the latent representations and their noisy versions, to embed the inputs into a latent space for clustering. As such the learned latent representations can be more robust to noise. Then the mutual information estimation is utilized to provide more discriminative information from the inputs. Furthermore, a deep spectral clustering method is applied to embed the latent representations into the eigenspace and subsequently clusters them, which can fully exploit the relationship between inputs to achieve optimal clustering results. Experimental results on benchmark datasets show that our method can significantly outperform state-of-the-art clustering approaches.