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 1011–1020 of 3304 papers

TitleStatusHype
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities—0
Learning Environment Models with Continuous Stochastic Dynamics—0
Feature Selection: A perspective on inter-attribute cooperation—0
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data—0
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—0
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction—0
Long-term Conversation Analysis: Exploring Utility and PrivacyCode0
Learning Nonautonomous Systems via Dynamic Mode Decomposition—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNECode0
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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