SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2751–2760 of 3304 papers

TitleStatusHype
An Information Theory-Based Feature Selection Framework for Big Data Under Apache SparkCode0
Kernel Feature Selection via Conditional Covariance MinimizationCode0
Kernel Scaling for Manifold Learning and Classification—0
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference—0
Dimensionality reduction with missing values imputation—0
Skip-Gram − Zipf + Uniform = Vector Additivity—0
Additive Component Analysis—0
Feature Hashing for Language and Dialect Identification—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering—0
Show:102550
← PrevPage 276 of 331Next →
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