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

Authors:
Hong Gu;Guangzhou Zhao;Jianliang Zhang
Affiliations:
College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China;College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China;College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
Venue:
Neurocomputing
Year:
2012

Citing 24
Cited 0

Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Data clustering: a review

ACM Computing Surveys (CSUR)
Support vector domain description

Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Cluster validity methods: part I

ACM SIGMOD Record
A Large Scale Clustering Scheme for Kernel K-Means

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Support vector clustering

The Journal of Machine Learning Research
Robust Real-Time Face Detection

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
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
Core Vector Machines: Fast SVM Training on Very Large Data Sets

The Journal of Machine Learning Research
Domain described support vector classifier for multi-classification problems

Pattern Recognition
A framework for statistical clustering with constant time approximation algorithms for K-median and K-means clustering

Machine Learning
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems

The Journal of Machine Learning Research
An Efficient Implementation of an Active Set Method for SVMs

The Journal of Machine Learning Research
Clustering Billions of Images with Large Scale Nearest Neighbor Search

WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
A cluster validity measure with a hybrid parameter search method for the support vector clustering algorithm

Pattern Recognition
Evolving parameters of multi-scale radial basis function kernels for support vector machines

ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Robust pseudo-hierarchical support vector clustering

SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Subset selection for efficient SVM tracking

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and 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	0.01

Visualization

Abstract

We propose a multi-scale, hierarchical framework to extend the scalability of support vector clustering (SVC). Based on multi-sphere support vector clustering, the clustering algorithm called multi-scale multi-sphere support vector clustering (MMSVC) 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 to describe 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 easy parameters tuning (robustness of parameters with respect to the clustering result) and the learning efficiency. We took 1.5 million tiny images to evaluate the method. Experimental result demonstrated that our method greatly improved the scalability and learning efficiency of support vector clustering.