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Dimensionality Reduction

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

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Papers

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Showing 651–675 of 3304 papers

TitleStatusHype
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities—0
Sparsifying dimensionality reduction of PDE solution data with Bregman learning—0
Oblivious subspace embeddings for compressed Tucker decompositions—0
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning—0
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance AnalysisCode0
Macroscopic Market Making Games via Multidimensional Decoupling Field—0
Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint tracking—0
VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region AnnotationCode0
Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from SimulationsCode0
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension—0
Noisy Data Visualization using Functional Data Analysis—0
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection—0
The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty QuantificationCode0
Random Subspace Local Projections—0
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective—0
A comparison of correspondence analysis with PMI-based word embedding methodsCode0
On the Connection Between Non-negative Matrix Factorization and Latent Dirichlet Allocation—0
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach—0
Estimates on the domain of validity for Lyapunov-Schmidt reduction—0
Enhancing Sufficient Dimension Reduction via Hellinger CorrelationCode0
Performance Examination of Symbolic Aggregate Approximation in IoT Applications—0
NUTS, NARS, and Speech—0
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE—0
Canonical Variates in Wasserstein Metric Space—0
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—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