Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Convex Optimization
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Learning sparse metrics via linear programming
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning a manifold-constrained map between image sets: applications to matching and pose estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian fields for semi-supervised regression and correspondence learning
Pattern Recognition
BoostMap: a method for efficient approximate similarity rankings
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A two-step framework for highly nonlinear data unfolding
Neurocomputing
Multiview Metric Learning with Global Consistency and Local Smoothness
ACM Transactions on Intelligent Systems and Technology (TIST)
Efficiency investigation of manifold matching for text document classification
Pattern Recognition Letters
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We study the problem of manifold alignment, which aims at "aligning" different data sets that share a similar intrinsic manifold provided some supervision. Unlike traditional methods that rely on pairwise correspondences between the two data sets, our method only needs some relative comparison information like "A is more similar to B than A is to C". This method provides a more flexible way to acquire the prior knowledge for alignment, thus is able to handle situations where corresponding pairs are hard or impossible to identify. We optimize our objective based on the graphs that give discrete approximations of the manifold. Further, the problem is formulated as a semi-definite programming(SDP) problem which can readily be solved. Finally, experimental results are presented to show the effectiveness of our method.