2007
NeurIPS
NeurIPS 2007
Iterative Non-linear Dimensionality Reduction with Manifold Sculpting
Abstract
Many algorithms have been recently developed for reducing dimensionality by projecting data onto an intrinsic non-linear manifold. Unfortunately, existing algo- rithms often lose significant precision in this transformation. Manifold Sculpting is a new algorithm that iteratively reduces dimensionality by simulating surface tension in local neighborhoods. We present several experiments that show Man- ifold Sculpting yields more accurate results than existing algorithms with both generated and natural data-sets. Manifold Sculpting is also able to benefit from both prior dimensionality reduction efforts.
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
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Trend Setter
- Representation Learning
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Hot Topic Early Bird
- manifold learning
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Data Science & Analytics > Methods > Data Mining
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Core Methods > Feature Learning
Artificial Intelligence > Core AI > Representation Learning