SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 1551–1560 of 3304 papers

TitleStatusHype
SpaceMAP: Visualizing Any Data in 2-dimension by Space Expansion—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—0
Learning Stochastic Representations of Physical Systems—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs—0
Dimension Reduction for Data with Heterogeneous MissingnessCode0
Non-Euclidean Self-Organizing Maps—0
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified