Dimensionality Reduction by Learning an Invariant Mapping
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- Type
- article
- Published
- 2006-06-17
- Cited by
- 5,899
- References
- 21
- Access
- Open access
- OpenAlex
- https://openalex.org/W2138621090
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:8281592
Keywords
Dimensionality reduction, Nonlinear dimensionality reduction, Manifold (fluid mechanics), Invariant (physics), Invariant manifold
References
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- Learning a kernel matrix for nonlinear dimensionality reduction
- Nonlinear dimensionality reduction by locally linear embedding.
- Gradient-based learning applied to document recognition
- Learning methods for generic object recognition with invariance to pose and lighting
- A Neural Support Vector Network architecture with adaptive kernels
- Normalized cuts and image segmentation
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem
- Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering
- Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
- Learning a similarity metric discriminatively, with application to face verification
- On Spectral Clustering: Analysis and an algorithm
- Signature Verification Using A "Siamese" Time Delay Neural Network
- Unsupervised Learning of Image Manifolds by Semidefinite Programming
- Unsupervised learning of image manifolds by semide .nite programming
- Normalized cuts and image segmentation
- Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
- GradientBased Learning Applied to Document Recognition
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- Place Classification With a Graph Regularized Deep Neural Network
- Towards Real-Time Image Understanding with Convolutional Networks. (Analyse sémantique des images en temps-réel avec des réseaux convolutifs)
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