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 1601–1610 of 3304 papers

TitleStatusHype
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
A Deep Signed Directional Distance Function for Object Shape Representation—0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty QuantificationCode0
Machine learning for assessing quality of service in the hospitality sector based on customer reviews—0
Measuring inter-cluster similarities with Alpha Shape TRIangulation in loCal Subspaces (ASTRICS) facilitates visualization and clustering of high-dimensional data—0
Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures—0
Identifying Layers Susceptible to Adversarial Attacks—0
WeightScale: Interpreting Weight Change in Neural Networks—0
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit—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