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2007 NIPS NeurIPS 2007

Learning the structure of manifolds using random projections

Abstract

We present a simple variant of the k-d tree which automatically adapts to intrinsic low dimensional structure in data.

📈 Trend Setter — Embedding Learning
🧭 Keyword Pioneer — k-d tree
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🐣 Hot Topic Early Bird — dimensionality reduction

Authors

Yoav Freund , Sanjoy Dasgupta , Mayank Kabra , Nakul Verma

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Core Methods > Representation Learning Machine Learning > Core Methods > Embedding Learning Mathematics & Optimization > Mathematics > Geometry Machine Learning > Core Methods > Dimensionality Reduction Machine Learning > Core Methods > Feature Learning

Keywords

dimensionality reduction feature learning manifold learning intrinsic dimension k-d tree manifold structure intrinsic dimensionality adaptive k-d tree low-dimensional structure random projection
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