SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 3201–3250 of 3304 papers

TitleStatusHype
Optimal High-order Tensor SVD via Tensor-Train Orthogonal IterationCode0
Supporting Multi-point Fan Design with Dimension ReductionCode0
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemiaCode0
Principal component analysis balancing prediction and approximation accuracy for spatial dataCode0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging DataCode0
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density EstimationCode0
SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality ReductionCode0
Sparse and Functional Principal Components AnalysisCode0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
Rerouting Connection: Hybrid Computer Vision Analysis Reveals Visual Similarity Between Indus and Tibetan-Yi Corridor Writing SystemsCode0
Gradient-based Sparse Principal Component Analysis with Extensions to Online LearningCode0
On the cross-validation bias due to unsupervised pre-processingCode0
Unsupervised Functional Data Analysis via Nonlinear Dimension ReductionCode0
Reservoir computing approaches for representation and classification of multivariate time seriesCode0
Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality ReductionCode0
Machine learning algorithms for three-dimensional mean-curvature computation in the level-set methodCode0
A Big Data Architecture for Early Identification and Categorization of Dark Web SitesCode0
Machine Learning Based Forward Solver: An Automatic Framework in gprMaxCode0
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorizationCode0
Convex Formulations for Fair Principal Component AnalysisCode0
Graph Convolutional Networks Meet with High Dimensionality ReductionCode0
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised LearningCode0
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning ModelsCode0
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised LearningCode0
Machine learning discovery of new phases in programmable quantum simulator snapshotsCode0
Topologically Regularized Data EmbeddingsCode0
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance MatricesCode0
Hidden Convexity of Fair PCA and Fast Solver via Eigenvalue OptimizationCode0
Active Learning for Manifold Gaussian Process RegressionCode0
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion RecognitionCode0
A Graphical Model for Fusing Diverse Microbiome DataCode0
Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement LearningCode0
Topology-Preserving Dimensionality Reduction via Interleaving OptimizationCode0
GraphTSNE: A Visualization Technique for Graph-Structured DataCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddingsCode0
Revisiting Bayesian Autoencoders with MCMCCode0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
Master's Thesis: Out-of-distribution Detection with Energy-based ModelsCode0
Manifold Denoising by Nonlinear Robust Principal Component AnalysisCode0
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
K-Deep Simplex: Deep Manifold Learning via Local DictionariesCode0
Grassmann Stein Variational Gradient DescentCode0
Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General RelativityCode0
Lazy FSCA for Unsupervised Variable SelectionCode0
Bi-Sparse Unsupervised Feature SelectionCode0
GridDehazeNet: Attention-Based Multi-Scale Network for Image DehazingCode0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
Manifold learning in metric spacesCode0
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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