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 576–600 of 3304 papers

TitleStatusHype
Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison—0
Feature Selection Based on Orthogonal Constraints and Polygon Area—0
Inference for Regression with Variables Generated by AI or Machine Learning—0
Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals—0
A Two-Stage Dual-Path Framework for Text Tampering Detection and Recognition—0
Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space—0
Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing DataCode0
Training Artificial Neural Networks by Coordinate Search Algorithm—0
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps—0
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised LearningCode0
An enhanced Teaching-Learning-Based Optimization (TLBO) with Grey Wolf Optimizer (GWO) for text feature selection and clustering—0
Evaluating the Stability of Deep Learning Latent Feature Spaces—0
Fast Data-driven Greedy Sensor Selection for Ridge Regression—0
Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering—0
Guided Quantum Compression for High Dimensional Data ClassificationCode0
SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds—0
You can monitor your hydration level using your smartphone camera—0
Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Simple, unified analysis of Johnson-Lindenstrauss with applications—0
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting MachineCode0
Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification—0
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation—0
Multilinear Kernel Regression and Imputation via Manifold Learning—0
On Minimum Trace Factor Analysis - An Old Song Sung to a New Tune—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