Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization (1704.06327v3)

Abstract: Image clustering is one of the most important computer vision applications, which has been extensively studied in literature. However, current clustering methods mostly suffer from lack of efficiency and scalability when dealing with large-scale and high-dimensional data. In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments. DEPICT generally consists of a multinomial logistic regression function stacked on top of a multi-layer convolutional autoencoder. We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments. An alternating strategy is then derived to optimize the objective by updating parameters and estimating cluster assignments. Furthermore, we employ the reconstruction loss functions in our autoencoder, as a data-dependent regularization term, to prevent the deep embedding function from overfitting. In order to benefit from end-to-end optimization and eliminate the necessity for layer-wise pretraining, we introduce a joint learning framework to minimize the unified clustering and reconstruction loss functions together and train all network layers simultaneously. Experimental results indicate the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.

• The paper introduces DEPICT, a model that jointly optimizes convolutional autoencoder embeddings and relative entropy minimization to enhance clustering performance.
• It employs an end-to-end alternating optimization strategy that combines reconstruction and KL divergence losses for robust unsupervised clustering without pretraining.
• Experimental results on datasets like MNIST and USPS demonstrate improved accuracy and efficiency, underscoring its practical potential in real-world applications.

Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

The paper "Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization" presents a sophisticated approach to image clustering, addressing efficiency and scalability challenges associated with high-dimensional, large-scale datasets. This research proposes an innovative model named DEPICT (DEeP Embedded RegularIzed ClusTering), which integrates the strengths of discriminative clustering techniques and deep learning frameworks.

Key Contributions

DEPICT is designed around the core concept of mapping high-dimensional data into a discriminative embedding subspace while leveraging relative entropy minimization for precise cluster predictions. The model comprises a multinomial logistic regression function built upon a multi-layer convolutional autoencoder. A salient feature is the introduction of a clustering objective function that employs Kullback-Leibler (KL) divergence, reinforced by a regularization term concerning the frequency of cluster assignments.

The authors have implemented an alternating optimization strategy to iteratively update model parameters and cluster assignments. This facilitates end-to-end training without requiring labor-intensive layer-wise pretraining.

Methodology

The architecture of DEPICT comprises two main components: a convolutional autoencoder and a soft-max layer. The autoencoder's role is to encapsulate the non-linear data transformations, while the soft-max layer predicts probabilistic cluster assignments. By utilizing dropout and reconstruction loss functions, the model counters overfitting tendencies, a common issue in deep learning tasks.

Central to the model is its unified objective function, conclusively combining clustering and reconstruction losses. The KL divergence-based clustering loss ensures discrimination between clusters, while the reconstruction loss ensures the model remains true to the input data's manifold.

Experimental Insights

DEPICT's performance was extensively evaluated on established image datasets, including MNIST and USPS. Results demonstrated superior or competitive clustering accuracy and normalized mutual information (NMI) compared to state-of-the-art methods, notably showing improvements in speed and efficacy without necessitating hyper-parameter tuning. This is particularly relevant for real-world applications where labeled data for tuning is unavailable.

Furthermore, DEPICT's ability to perform effectively without supervisory signals positions it as a practical solution for unsupervised clustering in diverse application domains.

Implications and Future Directions

Practically, DEPICT offers a robust solution for clustering problems involving complex, large-scale data, which are increasingly common in applications ranging from image and video analysis to bioinformatics. The inherent flexibility and scalability of the proposed approach make it well-suited for integration into existing pipelines or enhancement of current methods.

Theoretically, this work raises compelling considerations regarding the synergy between discriminative and generative model components within deep learning frameworks. Future research could explore extending this joint learning framework to other unsupervised learning tasks or incorporating additional constraints to further refine clustering accuracy.

In conclusion, DEPICT provides a methodologically sound and practically viable avenue towards efficient deep learning-based clustering, demonstrating notable advancements over previous methodologies in both performance scalability and computational efficiency.