SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1921–1930 of 3304 papers

TitleStatusHype
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Bayesian neural networks and dimensionality reduction—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Understanding Brain Dynamics for Color Perception using Wearable EEG headband—0
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks—0
Survey: Geometric Foundations of Data Reduction—0
Physical Action Categorization using Signal Analysis and Machine Learning—0
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion—0
Feature Clustering for Support Identification in Extreme Regions—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