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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 1576–1600 of 3304 papers

TitleStatusHype
Estimation of a function of low local dimensionality by deep neural networks—0
Identifying Chemicals Through Dimensionality Reduction—0
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data—0
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products—0
Identifying Layers Susceptible to Adversarial Attacks—0
Identifying manifolds underlying group motion in Vicsek agents—0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—0
Identifying semantic relations in a specialized corpus through distributional analysis of a cooccurrence tensor—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
"I know it when I see it". Visualization and Intuitive Interpretability—0
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs—0
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization—0
An efficient real-time target tracking algorithm using adaptive feature fusion—0
Image Classification by Feature Dimension Reduction and Graph based Ranking—0
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