Parametric umap embeddings for representation and semisupervised learning

Parametric umap embeddings for representation and semisupervised learning

UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data. The UMAP algorithm consists of two steps: (1) computing a graphical representation of a data set (fuzzy simplicial co...

Full description

Bibliographic Details
Main Authors: Gentner, T.Q (Author), McInnes, L. (Author), Sainburg, T. (Author)
Format: Article
Language:English
Published: MIT Press Journals 2021
Subjects:
Algebraic topology
article
autoencoder
classifier
Data set
Dimensionality reduction algorithms
embedding
Embeddings
Geometry
Gradient methods
Graph-based
Graphic methods
Graphical representations
learning
Low dimensional embedding
Nonparametrics
Optimization
Riemannian geometry
Stochastic systems
Structured data
Topology
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items