SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 501–510 of 3304 papers

TitleStatusHype
Machine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models—0
Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave GlitchesCode0
Variational Bayesian surrogate modelling with application to robust design optimisation—0
Iterative Cluster Harvesting for Wafer Map Defect Patterns—0
Distributional Principal AutoencodersCode1
Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification—0
Formation-Controlled Dimensionality Reduction—0
Quiver Laplacians and Feature SelectionCode0
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph EmbeddingCode1
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform—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