Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning

TL;DR

This paper introduces a novel deep adaptive fuzzy clustering method that combines fuzzy clustering with deep neural networks for improved unsupervised image representation and clustering performance.

Contribution

It proposes a joint deep fuzzy clustering model with iterative optimization, integrating fuzzy membership and deep feature learning for enhanced unsupervised clustering.

Findings

Outperforms state-of-the-art deep clustering methods in various datasets.

Achieves better reconstruction and clustering quality.

Demonstrates effective joint optimization of deep features and fuzzy clustering.

Abstract

Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the re-sampled data from estimated bottleneck space have consistent clustering properties to progressively improve the deep clustering model. Comprehensive experiments on a variety of datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality when compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments.