SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1331–1340 of 3304 papers

TitleStatusHype
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey—0
Error-Correcting Neural Networks for Two-Dimensional Curvature Computation in the Level-Set MethodCode0
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
Knowledge Base Index Compression via Dimensionality and Precision Reduction—0
Encoding large information structures in linear algebra and statistical modelsCode0
Systematic analysis reveals key microRNAs as diagnostic and prognostic factors in progressive stages of lung cancer—0
An efficient aggregation method for the symbolic representation of temporal dataCode1
SLISEMAP: Supervised dimensionality reduction through local explanationsCode1
Feature Space Hijacking Attacks against Differentially Private Split Learning—0
An Introduction to Autoencoders—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