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Manifold Approximation by Moving Least-Squares Projection (MMLS)
Barak Sober
*
,
David Levin
*
Corresponding author for this work
Department of Theoretical Mathematics
School of Mathematical Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
16
Scopus citations
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Keyphrases
Dimensionality Reduction
100%
Nonlinear Dimensionality Reduction
100%
Low-dimensional Manifolds
100%
Moving Least Squares
100%
Manifold Approximation
100%
Least-squares Projection
100%
High-dimensional Data
50%
Manifold Learning
50%
High Dimension
50%
Approximation Algorithms
50%
Application Data
50%
Real-world Application
50%
Computationally Efficient
50%
Large Dimension
50%
Low Dimensionality
50%
Dimensionality Problem
50%
Curse of Dimensionality
50%
Degree of Approximation
50%
Big Data Analysis
50%
Data Cloud
50%
Infinitely Smooth
50%
Scattered Data Points
50%
Analytic Knowledge
50%
Local Polynomial Approximation
50%
Mathematics
Manifold
100%
Moving Least Squares
100%
Dimensional Manifold
75%
Data Point
50%
Approximates
25%
Real Life
25%
Polynomial Approximation
25%
Dimensional Data
25%
Approximation Order
25%
Input Data
25%
Submanifold
25%
Curse of Dimensionality
25%
Approximant
25%
Big Data Analysis
25%