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 2851–2860 of 3304 papers

TitleStatusHype
bigMap: Big Data Mapping with Parallelized t-SNE—0
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery—0
Binary Matrix Factorisation via Column Generation—0
Binding constants of membrane-anchored receptors and ligands: a general theory corroborated by Monte Carlo simulations—0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
Black-Box k-to-1-PCA Reductions: Theory and Applications—0
Blind signal decomposition of various word embeddings based on join and individual variance explained—0
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—0
Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction—0
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets—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