SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1431–1440 of 3304 papers

TitleStatusHype
Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers—0
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
Learning Stochastic Representations of Physical Systems—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis—0
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks—0
SpaceMAP: Visualizing Any Data in 2-dimension by Space Expansion—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs—0
Show:102550
← PrevPage 144 of 331Next →
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