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 451–500 of 3304 papers

TitleStatusHype
Neural Networks Perform Sufficient Dimension ReductionCode0
Generative Modeling: A Review—0
Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data—0
Bi-Sparse Unsupervised Feature SelectionCode0
A Unifying Family of Data-Adaptive Partitioning Algorithms—0
Fast Multi-Group Gaussian Process Factor Models—0
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis—0
Computing Gram Matrix for SMILES Strings using RDKFingerprint and Sinkhorn-Knopp Algorithm—0
ICA-based Resting-State Networks Obtained on Large Autism fMRI Dataset ABIDECode0
Progressive Monitoring of Generative Model Training Evolution—0
CSI Compression using Channel Charting—0
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian OptimizationCode0
Representation learning of dynamic networks—0
Alternative Channel Charting Techniques in Cellular Wireless Communications—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities—0
A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition—0
Nested Diffusion Models Using Hierarchical Latent Priors—0
A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing—0
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management—0
An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction—0
Traversing the Subspace of Adversarial Patches—0
Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders—0
Noncommutative Model Selection for Data Clustering and Dimension Reduction Using Relative von Neumann Entropy—0
Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling—0
Autoencoder Enhanced Realised GARCH on Volatility Forecasting—0
HiCat: A Semi-Supervised Approach for Cell Type Annotation—0
Swin fMRI Transformer Predicts Early Neurodevelopmental Outcomes from Neonatal fMRI—0
Circuit design in biology and machine learning. II. Anomaly detection—0
FAST-Splat: Fast, Ambiguity-Free Semantics Transfer in Gaussian Splatting—0
Exploring the Manifold of Neural Networks Using Diffusion Geometry—0
Hierarchical Trait-State Model for Decoding Dyadic Social Interactions—0
Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures—0
K-means Derived Unsupervised Feature Selection using Improved ADMM—0
Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting—0
Feature Selection for Network Intrusion Detection—0
Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear FormsCode0
Towards a Fairer Non-negative Matrix Factorization—0
AstroM^3: A self-supervised multimodal model for astronomy—0
Expected Information Gain Estimation via Density Approximations: Sample Allocation and Dimension Reduction—0
White-Box Diffusion Transformer for single-cell RNA-seq generationCode0
ClusterGraph: a new tool for visualization and compression of multidimensional dataCode0
Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model—0
Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach—0
On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution DataCode0
Theoretically informed selection of latent activation in autoencoder based recommender systems—0
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—0
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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