SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1581–1590 of 3304 papers

TitleStatusHype
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data AnalysisCode0
Efficient channel charting via phase-insensitive distance computation—0
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
On Geodesic Distances and Contextual Embedding Compression for Text ClassificationCode0
Chasing Collective Variables using Autoencoders and biased trajectories—0
Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering TransformsCode1
Towards Exploratory Landscape Analysis for Large-scale Optimization: A Dimensionality Reduction FrameworkCode0
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopyCode0
Compact and Effective Representations for Sketch-based Image Retrieval—0
Quantum algorithms for SVD-based data representation and analysisCode0
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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