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 1041–1050 of 3304 papers

TitleStatusHype
Efficiently Computing Similarities to Private Datasets—0
A new set of cluster driven composite development indicators—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data—0
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
A Comparison Study on Nonlinear Dimension Reduction Methods with Kernel Variations: Visualization, Optimization and Classification—0
Efficient GPU implementation of randomized SVD and its applications—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints—0
EigenGS Representation: From Eigenspace to Gaussian Image Space—0
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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