SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2881–2890 of 3304 papers

TitleStatusHype
ClusterGraph: a new tool for visualization and compression of multidimensional dataCode0
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record DataCode0
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNECode0
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental AnalysisCode0
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed SystemsCode0
Introducing user-prescribed constraints in Markov chains for nonlinear dimensionality reductionCode0
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)Code0
Inverse Kernel DecompositionCode0
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and DimensionalityCode0
Empirical likelihood approach for high-dimensional moment restrictions with dependent dataCode0
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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