Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
A tutorial on spectral clustering
Statistics and Computing
Spectral clustering with inconsistent advice
Proceedings of the 25th international conference on Machine learning
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Semi-supervised graph clustering: a kernel approach
Machine Learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lower bounds for the partitioning of graphs
IBM Journal of Research and Development
Generalized re-weighting local sampling mean discriminant analysis
Pattern Recognition
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-Supervised Learning
Fast density-weighted low-rank approximation spectral clustering
Data Mining and Knowledge Discovery
Fast affinity propagation clustering: A multilevel approach
Pattern Recognition
Learning Spectral Embedding for Semi-supervised Clustering
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Graph dual regularization non-negative matrix factorization for co-clustering
Pattern Recognition
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Active selection of clustering constraints: a sequential approach
Pattern Recognition
Hi-index | 0.01 |
In recent years, semi-supervised clustering (SSC) has aroused considerable interests from the machine learning and data mining communities. In this paper we propose a novel SSC approach with enhanced spectral embedding (ESE), which not only considers the geometric structure information contained in data sets, but also can make use of the given side information such as pairwise constraints. Specially, we first construct a symmetry-favored k-NN graph, which is highly robust to noise and outliers, and can reflect the underlying manifold structures of data sets. Then we learn the enhanced spectral embedding towards an ideal data representation as consistent with the given pairwise constraints as possible. Finally, by using the regularization of spectral embedding we formulate learning the new data representation as a semidefinite-quadratic-linear programming (SQLP) problem, which can be efficiently solved. Experimental results on a variety of synthetic and real-world data sets show that our ESE approach outperforms the state-of-the-art SSC algorithms in terms of speed and quality on both vector-based and graph-based clustering.