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 1511–1520 of 3304 papers

TitleStatusHype
ExClus: Explainable Clustering on Low-dimensional Data Representations—0
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting—0
Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization—0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation—0
Data-driven Uncertainty Quantification in Computational Human Head Models—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Adaptive Weighted Multi-View Clustering—0
Merging Two Cultures: Deep and Statistical Learning—0
Autonomous Dimension Reduction by Flattening Deformation of Data Manifold under an Intrinsic Deforming Field—0
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer—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