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 1551–1560 of 3304 papers

TitleStatusHype
Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images—0
Hybrid Kronecker Product Decomposition and Approximation—0
Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models—0
Hybrid quantum-classical classifier based on tensor network and variational quantum circuit—0
Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic and Network Heterogeneity—0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Hybrid Subspace Learning for High-Dimensional Data—0
Finding Rule-Interpretable Non-Negative Data Representation—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