Semi-supervised learning with mixed knowledge information
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast semi-supervised clustering with enhanced spectral embedding
Pattern Recognition
A fast tri-factorization method for low-rank matrix recovery and completion
Pattern Recognition
Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification
Neural Processing Letters
Learning spectral embedding via iterative eigenvalue thresholding
Proceedings of the 21st ACM international conference on Information and knowledge management
Semi-supervised learning with nuclear norm regularization
Pattern Recognition
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In recent years, semi-supervised clustering (SSC) has aroused considerable interests from the machine learning and data mining communities. In this paper, we propose a novel semi-supervised clustering approach with enhanced spectral embedding (ESE) which not only considers structure information contained in data sets but also makes use of prior side information such as pair wise constraints. Specially, we first construct a symmetry-favored k-NN graph which is highly robust to noisy objects and can reflect the underlying manifold structure of data. Then we learn the enhanced spectral embedding towards an ideal representation as consistent with the pair wise constraints as possible. Finally, through taking advantage of Laplacian regularization, we formulate learning spectral representation as semi definite-quadratic-linear programs (SQLPs) under the squared loss function or small semi definitive programs (SDPs) under the hinge loss function, which both can be efficiently solved. Experimental results on a variety of synthetic and real-world data sets show that our approach outperforms the state-of-the-art SSC algorithms on both vector-based and graph-based clustering.