Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Simple, robust, scalable semi-supervised learning via expectation regularization
Proceedings of the 24th international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Propagable Graph for Semisupervised Learning: Classification and Regression
IEEE Transactions on Knowledge and Data Engineering
Robust classification using structured sparse representation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Nonnegative sparse coding for discriminative semi-supervised learning
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Task-Driven Dictionary Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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Graph-based Semi-Supervised Learning (SSL) methods are the widely used SSL methods due to their high accuracy. They can well meet the manifold assumption with high computational cost, but don't meet the cluster assumption. In this paper, we propose a Semi-supervised learning via SPArse (SSPA) model. Since SSPA uses sparse matrix multiplication to depict the adjacency relations among samples, SSPA can approximate low dimensional manifold structure of samples with lower computational complexity than these graph-based SSL methods. Each column of this sparse matrix corresponds to one sparse representation of a sample. The rational is that the inner product of sparse representations can also be sparse under certain constraint. Since the dictionary in the SSPA model can depict the distribution of the entire samples, the sparse representation of a sample encodes its spatial location information. Therefore, in the SSPA model the manifold structure of samples is computed via their locations in the intrinsic geometry of the distribution instead of their feature vectors. In order to meet the cluster assumption, we propose an structured dictionary learning algorithm to explicitly reveal the cluster structure of the dictionary. We develop the SSPA algorithms with the structured dictionary besides non-structured one, and experiments show that our methods are efficient and outperform state-of-the-art graph-based SSL methods.