A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture
By Erxue Min, Xifeng Guo, Qiang Liu, Gen Zhang, Jianjing Cui, Jun Long. IEEE Access, 2018
507 citations recorded by the source corpus.
Clustering is a fundamental problem in many data-driven application domains, and clustering performance highly depends on the quality of data representation. Hence, linear or non-linear feature transformations have been extensively used to learn a better data representation for clustering. In recent years, a lot of works focused on using deep neural networks to learn a clustering-friendly representation, resulting in a significant increase of clustering performance. In this paper, we give a systematic survey of clustering with deep learning in views of architecture. Specifically, we first introduce the preliminary knowledge for better understanding of this field. Then, a taxonomy of clustering with deep learning is proposed and some representative methods are introduced. Finally, we propose some interesting future opportunities of clustering with deep learning and give some conclusion remarks.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/d9e9ef4c91134a90704f2fe0722fbec8995734ab
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- S. M. Mousavi
- Gregory C. Beroza
- Antonio J. Plaza
- Javier Plaza
- Juan Mario Haut
- Mercedes Eugenia Paoletti
- Weiqiang Zhu
- Jianwei Ma
In Computer Vision and Pattern Recognition
- Juan Mario Haut
- Mercedes Eugenia Paoletti
- Javier Plaza
- Gregory C. Beroza
- Antonio J. Plaza
- S. M. Mousavi
Citations in the corpus
- Deep learning classifiers for hyperspectral imaging: A review (2019, methodology): “In fact, unsupervised DNN models are usually employed for clustering (Xie et al., 2016; Tian et al., 2017; Shaham et al., 2018; Min et al., 2018), anomaly detection (Penttilä, 2017; Ma et al.”
- Deep-learning seismology (2022, methodology): “The intelligent feature extraction and dimensional reduction mechanism of this new group of DNNs ( 19 , 124 ) render them well suited for exploratory analyses of high-dimensional seismic data such as DAS.”