SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2411–2420 of 3304 papers

TitleStatusHype
Multi-Objective Evolutionary approach for the Performance Improvement of Learners using Ensembling Feature selection and Discretization Technique on Medical data—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors - Extended Version—0
Multiple testing for outlier detection in functional data—0
Multi-point dimensionality reduction to improve projection layout reliability—0
Multiscale Flow for Robust and Optimal Cosmological Analysis—0
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data—0
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors—0
Multi-Space Evolutionary Search for Large-Scale Optimization—0
Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network—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