Name
Abstract | ||
|---|---|---|
In this paper, we propose a general framework DeepCluster to integrate traditional clustering methods into deep learning (DL) models and adopt Alternating Direction of Multiplier Method (ADMM) to optimize it. While most existing DL based clustering techniques have separate feature learning (via DL) and clustering (with traditional clustering methods), DeepCluster simultaneously learns feature representation and does cluster assignment under the same framework. Furthermore, it is a general and flexible framework that can employ different networks and clustering methods. We demonstrate the effectiveness of DeepCluster by integrating two popular clustering methods: K-means and Gaussian Mixture Model (GMM) into deep networks. The experimental results shown that our method can achieve state-of-the-art performance on learning representation for clustering analysis. Code and data related to this chapter are available at: https://github.com/JennyQQL/DeepClusterADMM-Release. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-71246-8_49 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Computer science,Multiplier method,Artificial intelligence,Deep learning,Cluster analysis,Mixture model,Machine learning,Feature learning | Conference | 10535 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.39 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| k tian | 1 | 18 | 5.11 |
| Shuigeng Zhou | 2 | 2089 | 207.00 |
| Jihong Guan | 3 | 657 | 81.13 |