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 2241–2250 of 3304 papers

TitleStatusHype
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks—0
Let the Tree Decide: FABART A Non-Parametric Factor Model—0
Multi-Dimensional Scaling on Groups—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting—0
License Plate Recognition with Compressive Sensing Based Feature Extraction—0
LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval—0
Lifespan tree of brain anatomy: diagnostic values for motor and cognitive neurodegenerative diseases—0
Lifetime Ruin under High-watermark Fees and Drift Uncertainty—0
Show:102550
← PrevPage 225 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