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 376–400 of 3304 papers

TitleStatusHype
Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality ReductionCode0
Forward-Cooperation-Backward (FCB) learning in a Multi-Encoding Uni-Decoding neural network architecture—0
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor—0
Empirical likelihood approach for high-dimensional moment restrictions with dependent dataCode0
From Small to Large Language Models: Revisiting the Federalist PapersCode0
Achieving Fair PCA Using Joint Eigenvalue Decomposition—0
Rewards-based image analysis in microscopy—0
Number Representations in LLMs: A Computational Parallel to Human PerceptionCode0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Fréchet Cumulative Covariance Net for Deep Nonlinear Sufficient Dimension Reduction with Random Objects—0
ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact—0
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting—0
Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis—0
Challenges of Multi-Modal Coreset Selection for Depth PredictionCode0
Disentangled Latent Spaces for Reduced Order Models using Deterministic Autoencoders—0
A Neural Operator-Based Emulator for Regional Shallow Water Dynamics—0
Reverse Markov Learning: Multi-Step Generative Models for Complex Distributions—0
Dimension reduction methods, persistent homology and machine learning for EEG signal analysis of Interictal Epileptic DischargesCode0
Random Forest Autoencoders for Guided Representation Learning—0
BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression Classification—0
Lifespan tree of brain anatomy: diagnostic values for motor and cognitive neurodegenerative diseases—0
Data-Enabled Predictive Control for Flexible Spacecraft—0
A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs—0
scMamba: A Pre-Trained Model for Single-Nucleus RNA Sequencing Analysis in Neurodegenerative Disorders—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