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 1591–1600 of 3304 papers

TitleStatusHype
Efficient Contextual Representation Learning Without Softmax Layer—0
Efficient channel charting via phase-insensitive distance computation—0
Deep Learning for Size and Microscope Feature Extraction and Classification in Oral Cancer: Enhanced Convolution Neural Network—0
Split Semantic Detection in Sandplay Images—0
Brain Inspired Face Recognition: A Computational Framework—0
Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—0
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons—0
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction—0
Efficient Binary Embedding of Categorical Data using BinSketch—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