深度聚类

论文阅读：Structure-Driven Representation Learning for Deep ClusteringXiang Wang, Liping Jing, Huafeng Liu, and Jian Yu. 2023. Structure-Driven Representation Learning for Deep Clustering. ACM Trans. Knowl. Discov. Data 18, 1, Article 31 (January 2024), 25 pages. https://doi.org/10.1145/3623400

【论文阅读】SCGC : Self-supervised contrastive graph clustering论文地址：SCGC : Self-supervised contrastive graph clustering - ScienceDirect

论文阅读：Towards Faster Deep Graph Clustering via Efficient Graph Auto-Encoder论文地址：Towards Faster Deep Graph Clustering via Efficient Graph Auto-Encoder | ACM Transactions on Knowledge Discovery from Data

论文阅读：Deep Fusion Clustering Network With Reliable Structure Preservation论文地址：Deep Fusion Clustering Network With Reliable Structure Preservation | IEEE Journals & Magazine | IEEE Xplore

论文阅读：Deep divergence-based approach to clustering论文地址：main.pdf (sciencedirectassets.com)深度学习研究中的一个有前景的方向是通过优化判别损失函数，学习表示并同时发现无标签数据中的聚类结构。与监督式深度学习不同，这一研究方向尚处于起步阶段，如何设计和优化合适的损失函数以训练深度神经网络进行聚类仍然是一个开放性问题。我们对这一新兴领域的贡献是提出一种新的深度聚类网络，该网络利用信息论发散度量的判别能力，已被证明在传统聚类中具有有效性。我们提出了一种新颖的损失函数，结合了几何正则化约束，从而避免了结果聚类划分的退化结构。通