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 1301–1310 of 3304 papers

TitleStatusHype
Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization—0
FastSVD-ML-ROM: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications—0
SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling—0
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data—0
Principal Geodesic Analysis of Merge Trees (and Persistence Diagrams)—0
Deep Sufficient Representation Learning via Mutual Information—0
CausNet : Generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraintsCode0
Natural language processing for clusterization of genes according to their functions—0
Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation—0
Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions—0
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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