SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1101–1150 of 3304 papers

TitleStatusHype
IMU-based Modularized Wearable Device for Human Motion Classification—0
The G-invariant graph Laplacian—0
Multimodal and multicontrast image fusion via deep generative models—0
Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon ForecastingCode0
Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis—0
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
A Multimodal Data-driven Framework for Anxiety Screening—0
Evaluation of distance-based approaches for forensic comparison: Application to hand odor evidence—0
Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable ConstructionCode0
From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain AdaptationCode0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Health Monitoring of Movement Disorder Subject based on Diamond Stacked Sparse Autoencoder Ensemble Model—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Improving information retrieval through correspondence analysis instead of latent semantic analysis—0
Lightweight feature encoder for wake-up word detection based on self-supervised speech representation—0
Incentives and co-evolution: Steering linear dynamical systems with noncooperative agents—0
A primer on correlation-based dimension reduction methods for multi-omics analysisCode0
Entropic Wasserstein Component AnalysisCode0
Differential Privacy Meets Neural Network Pruning—0
A topological classifier to characterize brain states: When shape matters more than variance—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
Super-Resolution Neural OperatorCode0
CAMEL: Curvature-Augmented Manifold Embedding and Learning—0
Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications—0
Graph-based Extreme Feature Selection for Multi-class Classification Tasks—0
Large Deviations for Accelerating Neural Networks Training—0
In search of the most efficient and memory-saving visualization of high dimensional data—0
Wasserstein Projection Pursuit of Non-Gaussian Signals—0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
Deep Kernel Principal Component Analysis for Multi-level Feature LearningCode0
nSimplex Zen: A Novel Dimensionality Reduction for Euclidean and Hilbert SpacesCode0
Image Reconstruction via Deep Image Prior SubspacesCode0
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning ModelsCode0
PAC-Bayesian Generalization Bounds for Adversarial Generative ModelsCode0
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis—0
Linearized Wasserstein dimensionality reduction with approximation guarantees—0
Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction—0
Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations—0
Sparse Dimensionality Reduction Revisited—0
Dimension Reduction and MARSCode0
Dimension reduction and redundancy removal through successive Schmidt decompositions—0
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps—0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
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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