Matrix computations (3rd ed.)
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning the unified kernel machines for classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Spectral kernel learning for semi-supervised classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
SIAM Journal on Matrix Analysis and Applications
Semi-Supervised Learning
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Semisupervised kernel matrix learning by kernel propagation
IEEE Transactions on Neural Networks
Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fixed point and Bregman iterative methods for matrix rank minimization
Mathematical Programming: Series A and B
The minimum-rank gram matrix completion via modified fixed point continuation method
Proceedings of the 36th international symposium on Symbolic and algebraic computation
Laplacian Support Vector Machines Trained in the Primal
The Journal of Machine Learning Research
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
Learning Spectral Embedding for Semi-supervised Clustering
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
A fast tri-factorization method for low-rank matrix recovery and completion
Pattern Recognition
Semi-supervised learning with nuclear norm regularization
Pattern Recognition
Efficient kernel learning from side information using ADMM
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Integrating new knowledge sources into various learning tasks to improve their performance has recently become an interesting topic. In this paper we propose a novel semi-supervised learning (SSL) approach, called semi-supervised learning with Mixed Knowledge Information (SSL-MKI) which can simultaneously handle both sparse labeled data and additional pairwise constraints together with unlabeled data. Specifically, we first construct a unified SSL framework to combine the manifold assumption and the pairwise constraints assumption for classification tasks. Then we present a Modified Fixed Point Continuation (MFPC) algorithm with an eigenvalue thresholding (EVT) operator to learn the enhanced kernel matrix. Finally, we develop a two-stage optimization strategy and provide an efficient SSL approach that takes advantage of Laplacian spectral regularization: semi-supervised learning with Enhanced Spectral Kernel (ESK). Experimental results on a variety of synthetic and real-world datasets demonstrate the effectiveness of the proposed ESK approach.