TR2005-117
Nonrigid Embeddings for Dimensionality Reduction
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- "Nonrigid Embeddings for Dimensionality Reduction", European Conference on Machine Learning (ECML), October 2005, vol. 3720.BibTeX TR2005-117 PDF
- @inproceedings{Brand2005oct,
- author = {Brand, M.},
- title = {Nonrigid Embeddings for Dimensionality Reduction},
- booktitle = {European Conference on Machine Learning (ECML)},
- year = 2005,
- volume = 3720,
- month = oct,
- isbn = {3-540-29243-8},
- url = {https://www.merl.com/publications/TR2005-117}
- }
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- "Nonrigid Embeddings for Dimensionality Reduction", European Conference on Machine Learning (ECML), October 2005, vol. 3720.
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MERL Contact:
Abstract:
Spectral methods for embedding graphs and immersing data manifolds in low-dimensional speaces are notoriously unstable due to insufficient and/or numberically ill-conditioned constraint sets. Why show shy this is endemic to spectral methods, and develop low-complexity solutions for stiffening ill-conditioned problems and regulatizing ill-posed problems, with proofs of correctness. The regularization exploits sparse but complementary constraints on affine rigidity and edge lengths to obtain isometric embeddings. Am implemented algorithm is fast, accurate and industrial-strength: Experiments with problem sizes spanning four orders of magnitude show O (N) scaling. We demonstrate with speech data.
Related News & Events
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NEWS ECML 2005: publication by Matthew Brand Date: October 3, 2005
Where: European Conference on Machine Learning (ECML)
MERL Contact: Matthew BrandBrief- The paper "Nonrigid Embeddings for Dimensionality Reduction" by Brand, M. was presented at the European Conference on Machine Learning (ECML).