SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1151–1160 of 3304 papers

TitleStatusHype
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
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction—0
On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods—0
Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation—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