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 1981–1990 of 3304 papers

TitleStatusHype
Memory-efficient training with streaming dimensionality reduction—0
ivis Dimensionality Reduction Framework for Biomacromolecular SimulationsCode3
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
Understanding Aesthetic Evaluation using Deep Learning—0
Latent regularization for feature selection using kernel methods in tumor classificationCode0
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer—0
Estimating a Brain Network Predictive of Stress and Genotype with Supervised AutoencodersCode0
TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural NetworksCode0
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach—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