Performance of optical flow techniques
International Journal of Computer Vision
Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
An equivalence between sparse approximation and support vector machines
Neural Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Fast Dual Algorithm for Kernel Logistic Regression
Machine Learning
A Comparison of Affine Region Detectors
International Journal of Computer Vision
On Learning Vector-Valued Functions
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Robust multi-task learning with t-processes
Proceedings of the 24th international conference on Machine learning
International Journal of Computer Vision
A Sparse Regularized Least-Squares Preference Learning Algorithm
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Stochastic gradient descent training for L1-regularized log-linear models with cumulative penalty
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Learning with generalization capability by kernel methods of bounded complexity
Journal of Complexity
Rejecting Mismatches by Correspondence Function
International Journal of Computer Vision
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
Vlfeat: an open and portable library of computer vision algorithms
Proceedings of the international conference on Multimedia
Point Set Registration: Coherent Point Drift
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust method for vector field learning with application to mismatch removing
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Comparison of worst case errors in linear and neural network approximation
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
Robust Estimation of Nonrigid Transformation for Point Set Registration
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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In vector field learning, regularized kernel methods such as regularized least-squares require the number of basis functions to be equivalent to the training sample size, N. The learning process thus has O(N^3) and O(N^2) in the time and space complexity, respectively. This poses significant burden on the vector learning problem for large datasets. In this paper, we propose a sparse approximation to a robust vector field learning method, sparse vector field consensus (SparseVFC), and derive a statistical learning bound on the speed of the convergence. We apply SparseVFC to the mismatch removal problem. The quantitative results on benchmark datasets demonstrate the significant speed advantage of SparseVFC over the original VFC algorithm (two orders of magnitude faster) without much performance degradation; we also demonstrate the large improvement by SparseVFC over traditional methods like RANSAC. Moreover, the proposed method is general and it can be applied to other applications in vector field learning.