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 2701–2710 of 3304 papers

TitleStatusHype
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks—0
A note on concentration inequality for vector-valued martingales with weak exponential-type tails—0
A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method—0
A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy—0
A Novel Approach for Intrinsic Dimension Estimation—0
A Novel Approach for Single Gene Selection Using Clustering and Dimensionality Reduction—0
A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique—0
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography—0
A Novel Denoising Technique and Deep Learning Based Hybrid Wind Speed Forecasting Model for Variable Terrain Conditions—0
A novel extension of Generalized Low-Rank Approximation of Matrices based on multiple-pairs of transformations—0
Show:102550
← PrevPage 271 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