重庆工商大学学报（自然科学版）2025，Vol.42Issue(3)：52-62,11.DOI:10.16055/j.issn.1672-058X.2025.0003.007
基于狄利克雷变分自编码的深度嵌入聚类
Deep Embedded Clustering Based on Dirichlet Variational Autoencoder
摘要
Abstract
Objective Deep neural network-based clustering models,capable of learning effective features from raw data,have received widespread attention in various unsupervised applications.Existing autoencoder-based clustering models lack generative ability and generally use Gaussian distribution as a prior,limiting the expression of multimodal features.This paper proposes a deeply embedded clustering model-DVADEC(Deep Embedded Clustering based on Dirichlet Variational Autoencoder),which integrates the representation learning capability of Dirichlet variational autoencoder and the clustering capability of embedded clustering into a unified model.Methods Firstly,during the pre-training phase,the multimodal nature of Dirichlet distribution is utilized as a prior distribution to guide the learning process of latent variables.Then,the trained weights are loaded into the clustering model,and class assignments are performed by embedding clustering layers in the latent space.Finally,the network is fine-tuned through alternating optimization of the objective function to enhance clustering results.Results Experimental results demonstrate that the DVADEC model exhibits good clustering performance on four benchmark datasets,achieving an accuracy of 97.13％on the MNIST image dataset and an accuracy of 80.1％on the REUTER-10k text dataset.Furthermore,visualization results demonstrate clear separability of latent features,and samples generated based on features exhibit distinct,smooth,and diverse contours.Conclusion The DVADEC model integrates generative capability and the ability to express multimodal features,significantly enhancing feature extraction and clustering performance.It provides new perspectives and technical means for the fields of data mining and pattern recognition.关键词
深度聚类/无监督学习/神经网络/狄利克雷分布/变分自编码Key words
deep clustering/unsupervised learning/neural networks/Dirichlet distribution/variational autoencoder分类
计算机与自动化引用本文复制引用
李必嘉,吴昊旻..基于狄利克雷变分自编码的深度嵌入聚类[J].重庆工商大学学报（自然科学版）,2025,42(3):52-62,11.基金项目
重庆师范大学研究生科研创新项目(YKC23032). （YKC23032）
