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 2871–2880 of 3304 papers

TitleStatusHype
Bringing Order to Chaos: A Non-Sequential Approach for Browsing Large Sets of Found Audio Data—0
Broadband Beamforming via Linear Embedding—0
Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond—0
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis—0
BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION—0
CAMEL: Curvature-Augmented Manifold Embedding and Learning—0
Can Genetic Programming Do Manifold Learning Too?—0
Canonical Correlation Analysis of Datasets with a Common Source Graph—0
Canonical Variates in Wasserstein Metric Space—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—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