Deep Spectral Clustering using Dual Autoencoder Network (1904.13113v1)

Abstract: 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.

• The paper introduces a dual autoencoder network that enforces noise-robust latent representations through reconstruction and mutual information maximization.
• It employs a joint learning framework that integrates discriminative embedding with deep spectral clustering to optimize clustering outcomes.
• Empirical results on benchmarks like MNIST show superior ACC and NMI, outperforming traditional deep clustering methods.

Deep Spectral Clustering using Dual Autoencoder Network: A Comprehensive Overview

The paper "Deep Spectral Clustering using Dual Autoencoder Network" explores an innovative approach to unsupervised clustering by leveraging advances in deep learning. The authors propose a joint learning framework that integrates discriminative embedding and spectral clustering to enhance clustering performance across various datasets.

Overview of the Methodology

The core contribution of this paper is the introduction of a dual autoencoder network that facilitates the generation of robust and discriminative latent representations. This network introduces a novel mechanism that enforces a reconstruction constraint for both the original latent representations and their noise-perturbed counterparts. This approach ensures that the latent representations are not only resilient to noise but also capable of preserving critical input information. Additionally, the use of mutual information estimation further augments the discriminative capacity of the embeddings.

Following the embedding process, a deep spectral clustering method is applied. This method maps the latent space representations into an eigenspace that fully captures the relational information inherent in the input data, thereby optimizing the clustering results. The dual autoencoder and the spectral clustering network are optimized in a unified framework to ensure coherence between the embedding and clustering phases.

Key Contributions

1. Dual Autoencoder Network: This network is central to the paper's contributions, utilizing noise-disturbed reconstruction loss and mutual information maximization to produce robust latent representations. These representations facilitate the clustering process by enhancing the discriminative features extracted from raw data.
2. Joint Learning Framework: By concurrently optimizing the dual autoencoder and the deep spectral clustering network, the proposed method ensures that the latent representations are immediately suitable for effective clustering.
3. Empirical Validation: The authors provide extensive evaluations on five benchmark datasets, including MNIST, USPS, and Fashion-MNIST. The results demonstrate the superiority of the proposed method over contemporary state-of-the-art techniques in both NMI and ACC metrics.

Empirical Insights

The paper reports that the dual autoencoder approach significantly outperforms traditional deep clustering methods such as DEC, IDEC, and SpectralNet. On the MNIST-test dataset, for example, the proposed method achieves an ACC of 98%, surpassing previous best results from methods like DEPICT and JULE. This performance indicates the model's capability to learn more nuanced and representative embeddings that translate into improved clustering outcomes.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the method can benefit applications that require robust and effective clustering, such as image segmentation and digital media analysis. Theoretically, the integration of mutual information estimation in the learning of latent representations presents a promising avenue for further exploration. Future developments might consider extending this framework to handle more complex data distributions and further improving scalability.

In conclusion, the paper presents a robust approach to deep cluster analysis that effectively bridges the gap between embedding quality and clustering performance. The rigorous treatment and comprehensive experimentation underscore the potential of deep learning methodologies in advancing the state of unsupervised clustering.