SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1311–1320 of 3304 papers

TitleStatusHype
On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods—0
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction—0
Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation—0
Nonlinear Sufficient Dimension Reduction for Distribution-on-Distribution RegressionCode0
Horizontal and Vertical Attention in Transformers—0
Stacked Autoencoder Based Multi-Omics Data Integration for Cancer Survival PredictionCode0
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection—0
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data—0
A Hybrid Approach for Binary Classification of Imbalanced Data—0
Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs—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