SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1001–1050 of 3304 papers

TitleStatusHype
A Functional approach for Two Way Dimension Reduction in Time Series—0
Causal Deep Learning—0
Quantum Machine Learning Applied to the Classification of Diabetes—0
Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis—0
Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net—0
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton FormatsCode2
Topological Data Analysis of Spatial Patterning in Heterogeneous Cell Populations: Clustering and Sorting with Varying Cell-Cell Adhesion—0
ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight ConditionsCode0
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image GenerationCode1
Quantifying Extrinsic Curvature in Neural ManifoldsCode1
UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment—0
Anticancer Peptides Classification using Kernel Sparse Representation ClassifierCode0
Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction—0
The Underlying Correlated Dynamics in Neural Training—0
Two-sample test based on Self-Organizing Maps—0
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs—0
Information retrieval in single cell chromatin analysis using TF-IDF transformation methods—0
SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing—0
Signal enhancement for two-dimensional cryo-EM data processingCode0
Shining light on data: Geometric data analysis through quantum dynamics—0
Time-Efficient Reward Learning via Visually Assisted Cluster Ranking—0
Universal Feature Selection Tool (UniFeat): An Open-Source Tool for Dimensionality ReductionCode0
DimenFix: A novel meta-dimensionality reduction method for feature preservation—0
Spatio-Temporal Crop Aggregation for Video Representation Learning—0
Privacy-Preserving Federated Deep Clustering based on GAN—0
Compression supports low-dimensional representations of behavior across neural circuits—0
Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations—0
Identifying Chemicals Through Dimensionality Reduction—0
Transform Once: Efficient Operator Learning in Frequency DomainCode1
Efficient Feature Extraction for High-resolution Video Frame InterpolationCode1
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size DataCode1
Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications—0
Kernel PCA for multivariate extremes—0
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for ImagesCode0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
EVNet: An Explainable Deep Network for Dimension ReductionCode1
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant AnalysisCode0
A Generalized EigenGame with Extensions to Multiview Representation LearningCode0
Graceful Forgetting II. Data as a Process—0
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction—0
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection—0
Data Dimension Reduction makes ML Algorithms efficient—0
RetroMAE v2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language ModelsCode2
Topology of cognitive maps—0
Inverse Kernel DecompositionCode0
Minimalist Data Wrangling with PythonCode1
Bridging Fairness and Environmental Sustainability in Natural Language Processing—0
Dimension Reduction for Efficient Data-Enabled Predictive Control—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Genomics Data Analysis via Spectral Shape and Topology—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