Research Area: Machine Learning
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.
Keywords:
Clustering Framework
Deep Learning
Gaussian Mixture Mode
Alternating Direction of Multiplier Method
Machine Learning
Deep Learning
Author(s) Name: Kai Tian,Shuigeng Zhou,Jihong Guan
Journal name:
Conferrence name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Publisher name: Springer
DOI: 10.1007/978-3-319-71246-8_49
Volume Information: volume 10535
