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 2026–2050 of 3304 papers

TitleStatusHype
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements—0
t-viSNE: Interactive Assessment and Interpretation of t-SNE ProjectionsCode1
Fair Principal Component Analysis and Filter Design—0
Deep reconstruction of strange attractors from time seriesCode1
Stable Sparse Subspace Embedding for Dimensionality Reduction—0
Molecular Insights from Conformational Ensembles via Machine LearningCode1
Optimal Iterative Sketching with the Subsampled Randomized Hadamard Transform—0
NCVis: Noise Contrastive Approach for Scalable VisualizationCode1
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Optimal estimation of sparse topic models—0
ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making PathsCode0
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection—0
ShapeVis: High-dimensional Data Visualization at Scale—0
Unifying Deep Local and Global Features for Image SearchCode1
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality ReductionCode0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy—0
Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization—0
Upper bounds for Model-Free Row-Sparse Principal Component Analysis—0
Estimating Model Uncertainty of Neural Network in Sparse Information Form—0
MODiR: Multi-Objective Dimensionality Reduction for Joint Data Visualisation—0
Measuring group-separability in geometrical space for evaluation of pattern recognition and embedding algorithms—0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational AutoencodersCode0
Deep learning to discover and predict dynamics on an inertial manifoldCode0
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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