SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 29812990 of 3304 papers

TitleStatusHype
Quantum algorithms for SVD-based data representation and analysisCode0
Quantum Annealing for Robust Principal Component AnalysisCode0
Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problemCode0
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation ClassificationCode0
ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making PathsCode0
Learning GPLVM with arbitrary kernels using the unscented transformationCode0
The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality ReductionCode0
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversionCode0
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text SpatializationCode0
Quantum-inspired Interpretable Deep Learning Architecture for Text Sentiment AnalysisCode0
Show:102550
← PrevPage 299 of 331Next →

Benchmark Results

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