Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document clustering with prior knowledge
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Semi-supervised graph clustering: a kernel approach
Machine Learning
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Identifying and generating easy sets of constraints for clustering
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Expert Systems with Applications: An International Journal
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Ensemble multi-instance multi-label learning approach for video annotation task
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Rank-loss support instance machines for MIML instance annotation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlative multi-label multi-instance image annotation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.10 |
In multi-instance multi-label (MIML) learning, datasets are given in the form of bags, each of which contains multiple instances and is associated with multiple labels. This paper considers a novel instance clustering problem in MIML learning, where the bag labels are used as background knowledge to help group instances into clusters. The goal is to recover the class labels or to find the subclasses within each class. Prior work on constraint-based clustering focuses on pairwise constraints and cannot fully utilize the bag-level label information. We propose to encode the bag-label knowledge into soft bag constraints that can be easily incorporated into any optimization based clustering algorithm. As a specific example, we demonstrate how the bag constraints can be incorporated into a popular spectral clustering algorithm. Empirical results on both synthetic and real-world datasets show that the proposed method achieves promising performance compared to state-of-the-art methods that use pairwise constraints.