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 251–260 of 3304 papers

TitleStatusHype
Analyzing movies to predict their commercial viability for producers—0
A Discussion On the Validity of Manifold Learning—0
Exact Cluster Recovery via Classical Multidimensional Scaling—0
An Analysis of the t-SNE Algorithm for Data Visualization—0
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems—0
A clustering adaptive Gaussian process regression method: response patterns based real-time prediction for nonlinear solid mechanics problems—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach—0
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks—0
An Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images—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