Unsupervised Deep Embedding for Fuzzy Clustering

Deep fuzzy clustering employs neural networks to discover the low-dimensional embedding space of data, providing an effective solution to the clustering problem posed by high-dimensional data. Although some algorithms have achieved good results in application, the field still faces the following problems: the lack of clustering loss function limits the development of deep clustering, and most of them use the self-training strategy-based Kullback-Leibler (KL) divergence; some algorithms directly use conventional constrained clustering objective function as the loss function in deep models, and update network parameters alternately, the optimization process is cumbersome. Focusing on the issues mentioned above, this article first proposed an unconstrained fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula>-means algorithm that can be solved using gradient descent and then used it as the clustering loss function to obtain a novel deep fuzzy clustering model named unsupervised deep embedding for fuzzy clustering. The proposed model simultaneously learns the low-dimensional representation of data and performs fuzzy clustering. It updates parameters through gradient descent and backpropagation, achieving end-to-end optimization. The proposed algorithm's effectiveness and competitiveness are fully demonstrated through extensive experiments conducted on image and text datasets.