SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 161170 of 3304 papers

TitleStatusHype
DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensionality ReductionCode1
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context LearningCode1
Efficient Feature Extraction for High-resolution Video Frame InterpolationCode1
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering TransformsCode1
Estimating leverage scores via rank revealing methods and randomizationCode1
Extracting the main trend in a dataset: the Sequencer algorithmCode1
Fast and Accurate Network Embeddings via Very Sparse Random ProjectionCode1
Fast Network Embedding Enhancement via High Order Proximity ApproximationCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
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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