Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
A kernel view of the dimensionality reduction of manifolds
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Out-of-Sample Extrapolation of Learned Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Dimensionality Reduction with Local Spline Embedding
IEEE Transactions on Knowledge and Data Engineering
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Multimedia
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
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The successful applications of manifold learning in computer vision and multimedia research show that the geodesic distance along the manifold is more meaningful than Euclidean distance in the linear space. Therefore, in order to get better performance of image classification, it is preferable to have classifier defined on the low-dimensional manifold. However, most of the manifold learning algorithms do not provide explicit mapping of the unseen data. In this paper, we propose a framework of image classification with manifold learning for out-of-sample data. The method of local and global regressive mapping for manifold learning simultaneously learns the low-dimensional embedding of the input data and a mapping function for out-of-sample data extrapolation. The low-dimensional manifold embedding of large-scale images can be obtained by the mapping functions. Utilizing the supervised classifier, we predict class labels for test images in the learned low-dimensional manifold. Experiments on two large-scale image datasets demonstrate that the proposed framework has better performance of image classification than the kernelized dimension reduction methods.