Communication Dans Un Congrès Année : 1998

Dimensionality Reduction by Learning an Invariant Mapping

Résumé

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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hal-05094360 , version 1 (03-06-2025)

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Raia Hadsell, Sumit Chopra, Yann Lecun. Dimensionality Reduction by Learning an Invariant Mapping. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), 2006, New York, United States. pp.1735-1742, ⟨10.1109/CVPR.2006.100⟩. ⟨hal-05094360⟩
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