Guided learning for role discovery (GLRD): framework, algorithms, and applications
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure preserving non-negative matrix factorization for dimensionality reduction
Computer Vision and Image Understanding
Learning descriptive visual representation by semantic regularized matrix factorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation
Expert Systems with Applications: An International Journal
Computation of level lines of 4-/8-connectedness
Journal of Visual Communication and Image Representation
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Nonnegative matrix factorization (NMF) is a popular technique for finding parts-based, linear representations of nonnegative data. It has been successfully applied in a wide range of applications such as pattern recognition, information retrieval, and computer vision. However, NMF is essentially an unsupervised method and cannot make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Nonnegative Matrix Factorization (CNMF), which incorporates the label information as additional constraints. Specifically, we show how explicitly combining label information improves the discriminating power of the resulting matrix decomposition. We explore the proposed CNMF method with two cost function formulations and provide the corresponding update solutions for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications.