From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning non-redundant codebooks for classifying complex objects
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary
SIAM Journal on Imaging Sciences
Max-margin dictionary learning for multiclass image categorization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Sparse dictionary-based representation and recognition of action attributes
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We present an online semi-supervised dictionary learning algorithm for classification tasks. Specifically, we integrate the reconstruction error of labeled and unlabeled data, the discriminative sparse-code error, and the classification error into an objective function for online dictionary learning, which enhances the dictionary's representative and discriminative power. In addition, we propose a probabilistic model over the sparse codes of input signals, which allows us to expand the labeled set. As a consequence, the dictionary and the classifier learned from the enlarged labeled set yield lower generalization error on unseen data. Our approach learns a single dictionary and a predictive linear classifier jointly. Experimental results demonstrate the effectiveness of our approach in face and object category recognition applications.