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 1621–1630 of 3304 papers

TitleStatusHype
Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning—0
IMU-based Modularized Wearable Device for Human Motion Classification—0
Agnostically Learning Multi-index Models with Queries—0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
Incomplete Pivoted QR-based Dimensionality Reduction—0
A Convex Sparse PCA for Feature Analysis—0
A canonical correlation-based framework for performance analysis of radio access networks—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression—0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—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