MMSVC: an efficient unsupervised learning approach for large-scale datasets

Authors:
Hong Gu;Guangzhou Zhao;Jianliang Zhang
Affiliations:
College of Electric Engineering, Zhejiang University, Hangzhou, China;College of Electric Engineering, Zhejiang University, Hangzhou, China;College of Electric Engineering, Zhejiang University, Hangzhou, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Year:
2010

Citing 11
Cited 0

Cluster validity methods: part I

ACM SIGMOD Record
Support vector clustering

The Journal of Machine Learning Research
Support Vector Data Description

Machine Learning
Kernel k-means: spectral clustering and normalized cuts

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Improved Cluster Labeling Method for Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain described support vector classifier for multi-classification problems

Pattern Recognition
A cluster validity measure with a hybrid parameter search method for the support vector clustering algorithm

Pattern Recognition
The entire regularization path for the support vector domain description

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Structured One-Class Classification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a multi-scale, hierarchical framework to extend the scalability of support vector clustering (SVC). Based on the multi-sphere support vector clustering, the clustering algorithm called multi-scale multi-sphere support vector clustering (MMSVC) in this framework works in a coarse-to-fine and top-to-down manner. Given one parent cluster, the next learning scale is generated by a secant-like numerical algorithm. A local quantity called spherical support vector density (sSVD) is proposed as a cluster validity measure which describes the compactness of the cluster. It is used as a terminate term in our framework. When dealing with large-scale dataset, our method benefits from the online learning, easy parameters tuning and the learning efficiency. 1.5 million tiny images were used to evaluate the method. Experimental results demonstrate that the method greatly improves the scalability and learning efficiency of support vector clustering.