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 1141–1150 of 3304 papers

TitleStatusHype
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems—0
Factor Network Autoregressions—0
Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints—0
EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification—0
Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data—0
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19Code0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization—0
FastSVD-ML-ROM: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications—0
SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling—0
Show:102550
← PrevPage 115 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