Additive versus exponentiated gradient updates for linear prediction
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Document clustering by concept factorization
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Locally Consistent Concept Factorization for Document Clustering
IEEE Transactions on Knowledge and Data Engineering
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonnegative sparse coding for discriminative semi-supervised learning
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Non-negative matrix factorization as a feature selection tool for maximum margin classifiers
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Constrained Nonnegative Matrix Factorization for Image Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Concept factorization (CF) is a variant of non-negative matrix factorization (NMF). In CF, each concept is represented by a linear combination of data points, and each data point is represented by a linear combination of concepts. More specifically, each concept is represented by more than one data point with different weights, and each data point carries various weights called membership to represent their degrees belonging to that concept. However, CF is actually an unsupervised method without making use of prior information of the data. In this paper, we propose a novel semi-supervised concept factorization method, called Pairwise Constrained Concept Factorization (PCCF), which incorporates pairwise constraints into the CF framework. We expect that data points which have pairwise must-link constraints should have the same class label as much as possible, while data points with pairwise cannot-link constraints will have different class labels as much as possible. Due to the incorporation of the pairwise constraints, the learning quality of the CF has been significantly enhanced. Experimental results show the effectiveness of our proposed novel method in comparison to the state-of-the-art algorithms on several real world applications.