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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 1121–1130 of 3304 papers

TitleStatusHype
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data—0
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A HumanCode0
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images—0
A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmsCode1
Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional AutoencodersCode1
A novel approach for Fair Principal Component Analysis based on eigendecompositionCode0
GANs and Closures: Micro-Macro Consistency in Multiscale Modeling—0
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails—0
Convergent autoencoder approximation of low bending and low distortion manifold embeddingsCode0
A Graphical Model for Fusing Diverse Microbiome DataCode0
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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