Machine Learning - Special issue on inductive transfer
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Convex Optimization
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
The class imbalance problem: A systematic study
Intelligent Data Analysis
Domain Adaptation of Conditional Probability Models Via Feature Subsetting
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Semi-supervised graph clustering: a kernel approach
Machine Learning
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A convex formulation for learning shared structures from multiple tasks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Extracting discriminative concepts for domain adaptation in text mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-task classification with infinite local experts
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data
IEEE Transactions on Signal Processing - Part II
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Clustering is a fundamental topic in pattern recognition and machine learning research. Traditional clustering methods deal with a single clustering task on a single data set. However, in many real applications, multiple similar clustering tasks are involved simultaneously, e.g., clustering clients of different shopping websites, in which data of different subjects are collected for each task. These tasks are cross-domains but closely related. It is proved that we can improve the individual performance of each clustering task by appropriately utilizing the underling relation. In this paper, we will propose a new approach, which performs multiple related clustering tasks simultaneously through domain adaptation. A shared subspace will be learned through domain adaptation, where the gap of distributions among tasks is reduced, and the shared knowledge will be transferred through all tasks by exploiting the strengthened relation in the learned subspace. Then the object is set as the best clustering in both the original and learned spaces. An alternating optimization method is introduced and its convergence is theoretically guaranteed. Experiments on both synthetic and real data sets demonstrate the effectiveness of the proposed approach.