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 851–860 of 3304 papers

TitleStatusHype
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities—0
A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction—0
Differentially private sliced inverse regression in the federated paradigm—0
PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data AnalysisCode0
Multiscale Flow for Robust and Optimal Cosmological Analysis—0
Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving—0
Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization—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