Dimensionality Reduction by Learning an Invariant Mapping
Open accessAbstract
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE.
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- Authors
- Hadsell, R., Chopra, S., & LeCun, Y.
- Affiliations
- Courant Institute of Mathematical Sciences (US)
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Issue
- 11
- Pages
- 1735-1742
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2138621090
- Cited by
- 5,336
- References
- 20
- Citations by year
- 2026: 217 · 2025: 475 · 2024: 622 · 2023: 779 · 2022: 740 · 2021: 867 · 2020: 589 · 2019: 384 · 2018: 222 · 2017: 153 · 2016: 102 · 2015: 60 · 2014: 25 · 2013: 26 · 2012: 25
- FWCI
- 7.92
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Licence
- other-oa
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- dimensionality reduction, manifold learning, nonlinear mapping, neighborhood relationships, transformation invariance, locally linear embedding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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