Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The Effect of the Input Density Distribution on Kernel-based Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
Prototype vector machine for large scale semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An information-theoretic framework to aggregate a Markov chain
ACC'09 Proceedings of the 2009 conference on American Control Conference
Sparse Semi-supervised Learning Using Conjugate Functions
The Journal of Machine Learning Research
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
SIAM Journal on Scientific Computing
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Advances of modern science and engineering lead to unprecedented amount of data for information processing. Of particular interest is the semi-supervised learning, where very few training samples are available among large volumes of unlabeled data. Graph-based algorithms using Laplacian regularization have achieved state-of-the-art performance, but can induce huge memory and computational costs. In this paper, we introduce L"1-norm penalization on the low-rank factorized kernel for efficient, globally optimal model selection in graph-based semi-supervised learning. An important novelty is that our formulation can be transformed to a standard LASSO regression. On one hand, this makes it possible to employ advanced sparse solvers to handle large scale problems; on the other hand, a globally optimal subset of basis can be chosen adaptively given desired strength of penalizing model complexity, in contrast to some current endeavors that pre-determine the basis without coupling it with the learning task. Our algorithm performs competitively with state-of-the-art algorithms on a variety of benchmark data sets. In particular, it is orders of magnitude faster than exact algorithms and achieves a good trade-off between accuracy and scalability.