Initialization enhancer for non-negative matrix factorization
Engineering Applications of Artificial Intelligence
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Engineering Applications of Artificial Intelligence
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
About the relationship between ROC curves and Cohen's kappa
Engineering Applications of Artificial Intelligence
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data
Engineering Applications of Artificial Intelligence
Locality preserving nonnegative matrix factorization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective
IEEE Transactions on Knowledge and Data Engineering
Engineering Applications of Artificial Intelligence
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosted Learning of Visual Word Weighting Factors for Bag-of-Features Based Medical Image Retrieval
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
Domain Transfer Multiple Kernel Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-layer group sparse coding -- For concurrent image classification and annotation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Ensemble Manifold Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain Adaptation via Transfer Component Analysis
IEEE Transactions on Neural Networks
A clustering based feature selection method in spectro-temporal domain for speech recognition
Engineering Applications of Artificial Intelligence
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
A new feature selection algorithm and composite neural network for electricity price forecasting
Engineering Applications of Artificial Intelligence
Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules
Engineering Applications of Artificial Intelligence
Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting
Engineering Applications of Artificial Intelligence
Multitask multiclass support vector machines: Model and experiments
Pattern Recognition
Semi-Supervised Hashing for Large-Scale Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple graph regularized nonnegative matrix factorization
Pattern Recognition
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Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm - multiple-domain NMF (MDNMF) - was evaluated on two challenging multiple-domain learning problems - multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified.