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 2011–2020 of 3304 papers

TitleStatusHype
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case StudyCode0
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction—0
Regularized Pooling—0
Low-Rank Nonlinear Decoding of μ-ECoG from the Primary Auditory Cortex—0
Probing Criticality in Quantum Spin Chains with Neural Networks—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
The Information Bottleneck Problem and Its Applications in Machine Learning—0
Memory-efficient training with streaming dimensionality reduction—0
Spectral Learning on Matrices and Tensors—0
Multi-Objective Evolutionary approach for the Performance Improvement of Learners using Ensembling Feature selection and Discretization Technique on Medical data—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