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 976–1000 of 3304 papers

TitleStatusHype
An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns—0
Weighted Sparse Partial Least Squares for Joint Sample and Feature SelectionCode0
Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection—0
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization—0
Deep Learning Architecture for Motor Imaged Words—0
ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning ProblemsCode0
Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review—0
Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image SegmentationCode0
Visualization of Extremely Sparse Contingency Table by Taxicab Correspondence Analysis: A Case Study of Textual Data—0
Flashlight Search Medial Axis: A Pixel-Free Pore-Network Extraction Algorithm—0
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning—0
Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes—0
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images—0
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality ReductionCode0
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels—0
SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text—0
A Statistical View of Column Subset SelectionCode0
On Optimality in ROVir—0
Heuristic Hyperparameter Choice for Image Anomaly Detection—0
Two Approaches to Supervised Image Segmentation—0
A Computational Topology-based Spatiotemporal Analysis Technique for Honeybee AggregationCode0
Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation—0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—0
Company2Vec -- German Company Embeddings based on Corporate Websites—0
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text SpatializationCode0
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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