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 401–410 of 3304 papers

TitleStatusHype
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows—0
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
A Flag Decomposition for Hierarchical DatasetsCode0
Negative Dependence as a toolbox for machine learning : review and new developments—0
Study on Downlink CSI compression: Are Neural Networks the Only Solution?—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies—0
Geometric Machine Learning on EEG Signals—0
Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methodsCode0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—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