Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms
Proceedings of the 25th international conference on Machine learning
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Gaussian Processes for Object Categorization
International Journal of Computer Vision
Proximal Methods for Hierarchical Sparse Coding
The Journal of Machine Learning Research
Active Learning Based on Locally Linear Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold Adaptive Experimental Design for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Active Learning Methods for Interactive Image Retrieval
IEEE Transactions on Image Processing
Structured sparsity via alternating direction methods
The Journal of Machine Learning Research
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In many real world scenarios, active learning methods are used to select the most informative points for labeling to reduce the expensive human action. One direction for active learning is selecting the most representative points, ie., selecting the points that other points can be approximated by linear combination of the selected points. However, these methods fails to consider the local geometrical information of the data space. In this paper, we propose a novel framework named Active Learning via Neighborhood Reconstruction (ALNR) by taking into account the locality information directly during the selection. Specifically, for the linear reconstruction of target point, the nearer neighbors should have a greater effect and the selected points distant from the target point should be penalized severely. We further develop an efficient two-stage iterative procedure to solve the final optimization problem. Our empirical study shows encouraging results of the proposed algorithms in comparison to other state-of-the-art active learning algorithms on both synthetic and real visual data sets.