International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic manifold learning for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Negative Samples Analysis in Relevance Feedback
IEEE Transactions on Knowledge and Data Engineering
Laplacian optimal design for image retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Regularized regression on image manifold for retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Active learning with statistical models
Journal of Artificial Intelligence Research
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Variational inference with graph regularization for image annotation
ACM Transactions on Intelligent Systems and Technology (TIST)
Non-goal scene analysis for soccer video
Neurocomputing
A linear discriminant analysis method based on mutual information maximization
Pattern Recognition
Hessian optimal design for image retrieval
Pattern Recognition
Summarizing tourist destinations by mining user-generated travelogues and photos
Computer Vision and Image Understanding
Neighborhood preserving regression for image retrieval
Neurocomputing
Locally regressive G-optimal design for image retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Transfer latent variable model based on divergence analysis
Pattern Recognition
Fully affine invariant SURF for image matching
Neurocomputing
Interactive cartoon reusing by transfer learning
Signal Processing
A novel method for image retrieval based on structure elements' descriptor
Journal of Visual Communication and Image Representation
G-Optimal Feature Selection with Laplacian regularization
Neurocomputing
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In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels are expensive and the challenge is, thus, to determine which unlabeled samples would be the most informative (i.e., improve the classifier the most) if they were labeled and used as training samples. Particularly, we consider the problem of active learning of a regression model in the context of experimental design. Classical optimal experimental design approaches are based on least square errors over the measured samples only. They fail to take into account the unmeasured samples. In this paper, we propose a novel active learning algorithm which operates over graphs. Our algorithm is based on a graph Laplacian regularized regression model which simultaneously minimizes the least square error on the measured samples and preserves the local geometrical structure of the data space. By constructing a nearest neighbor graph, the geometrical structure of the data space can be described by the graph Laplacian. We discuss how results from the field of optimal experimental design may be used to guide our selection of a subset of data points, which gives us the most amount of information. Experiments demonstrate its superior performance in comparison with conventional algorithms.