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 521–530 of 3304 papers

TitleStatusHype
Comparison of Methods in Skin Pigment Decomposition—0
DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object DetectionCode0
Enhancing Dimension-Reduced Scatter Plots with Class and Feature CentroidsCode0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach—0
Uncovering Temporal Patterns in Visualizations of High-Dimensional Data—0
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation ModelsCode0
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons—0
Representatividad Muestral en la Incertidumbre Simétrica Multivariada para la Selección de Atributos—0
Targeted Visualization of the Backbone of Encoder LLMsCode1
Show:102550
← PrevPage 53 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