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 1801–1810 of 3304 papers

TitleStatusHype
Instance Space Analysis for the Car Sequencing Problem—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
Difficulty in estimating visual information from randomly sampled images—0
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques—0
Clustering high dimensional meteorological scenarios: results and performance index—0
A t-SNE Based Classification Approach to Compositional Microbiome Data—0
Recovery of Linear Components: Reduced Complexity Autoencoder Designs—0
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation—0
Spatial noise-aware temperature retrieval from infrared sounder data—0
Generating semantic maps through multidimensional scaling: linguistic applications and theory—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