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 926–950 of 3304 papers

TitleStatusHype
Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification—0
Feature Space Sketching for Logistic Regression—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Data Augmentation For Label Enhancement—0
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation—0
Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity PricesCode0
An evaluation framework for dimensionality reduction through sectional curvatureCode0
Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable ConstructionCode0
A Multimodal Data-driven Framework for Anxiety Screening—0
Evaluation of distance-based approaches for forensic comparison: Application to hand odor evidence—0
From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain AdaptationCode0
Health Monitoring of Movement Disorder Subject based on Diamond Stacked Sparse Autoencoder Ensemble Model—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Lightweight feature encoder for wake-up word detection based on self-supervised speech representation—0
Improving information retrieval through correspondence analysis instead of latent semantic analysis—0
A primer on correlation-based dimension reduction methods for multi-omics analysisCode0
Incentives and co-evolution: Steering linear dynamical systems with noncooperative agents—0
Entropic Wasserstein Component AnalysisCode0
Differential Privacy Meets Neural Network Pruning—0
Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction—0
Sufficient dimension reduction for feature matrices—0
A topological classifier to characterize brain states: When shape matters more than variance—0
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
Super-Resolution Neural OperatorCode0
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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