SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 781–790 of 3304 papers

TitleStatusHype
Sparse Models for Machine Learning—0
Class-constrained t-SNE: Combining Data Features and Class ProbabilitiesCode0
Is Deep Learning Network Necessary for Image Generation?—0
The Potential of Quantum Techniques for Stock Price Prediction—0
Free Lunch for Gait Recognition: A Novel Relation Descriptor—0
Ultra Dual-Path Compression For Joint Echo Cancellation And Noise SuppressionCode1
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction—0
An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns—0
Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and InterpretationCode0
Weighted Sparse Partial Least Squares for Joint Sample and Feature SelectionCode0
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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