SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 111120 of 3304 papers

TitleStatusHype
A New Basis for Sparse Principal Component AnalysisCode1
Approximating Likelihood Ratios with Calibrated Discriminative ClassifiersCode1
Going Beyond T-SNE: Exposing whatlies in Text EmbeddingsCode1
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size DataCode1
A Spectral Method for Assessing and Combining Multiple Data VisualizationsCode1
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsCode1
HUMAP: Hierarchical Uniform Manifold Approximation and ProjectionCode1
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation VectorsCode1
An Additive Autoencoder for Dimension EstimationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified