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Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 3304 papers

TitleStatusHype
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
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantificationCode1
A comparison of correspondence analysis with PMI-based word embedding methodsCode0
Estimates on the domain of validity for Lyapunov-Schmidt reduction—0
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach—0
On the Connection Between Non-negative Matrix Factorization and Latent Dirichlet Allocation—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
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—0
Canonical Variates in Wasserstein Metric Space—0
Embedding Compression for Efficient Re-Identification—0
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization—0
Rank Reduction Autoencoders—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
Input Guided Multiple Deconstruction Single Reconstruction neural network models for Matrix Factorization—0
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces—0
Automated Anomaly Detection on European XFEL Klystrons—0
Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging—0
An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—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