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 2561–2570 of 3304 papers

TitleStatusHype
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility—0
A dynamical systems based framework for dimension reduction—0
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction—0
A Faster Approach to Spiking Deep Convolutional Neural Networks—0
A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization—0
A fast, universal algorithm to learn parametric nonlinear embeddings—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
A Flexible Iterative Framework for Consensus Clustering—0
A framework for streamlined statistical prediction using topic models—0
A Fully Convolutional Network for MR Fingerprinting—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