Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Prototype vector machine for large scale semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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Graph-based semi-supervised learning algorithms have attracted increasing attentions recently due to their superior performance in dealing with abundant unlabeled data and limited labeled data via the label propagation. The principle issue of constructing a graph is how to accurately measure the similarity between two data examples. In this paper, we propose a novel approach to measure the similarities among data points by means of the local linear reconstruction of their corresponding sparse codes. Clearly, the sparse codes of data examples not only preserve their local manifold semantics but can significantly boost the discriminative power among different classes. Moreover, the sparse property helps to dramatically reduce the intensive computation and storage requirements. The experimental results over the well-known dataset Caltech-101 demonstrate that our proposed similarity measurement method delivers better performance of the label propagation.