SOTAVerified

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 2801–2825 of 3304 papers

TitleStatusHype
Manifold Coordinates with Physical MeaningCode0
Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecastingCode0
Incorporating Texture Information into Dimensionality Reduction for High-Dimensional ImagesCode0
Scalable Manifold Learning for Big Data with Apache SparkCode0
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesCode0
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for ImagesCode0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and CheckingCode0
Adversarial Canonical Correlation AnalysisCode0
Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) dataCode0
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projectionsCode0
A Mixture of Matrix Variate Bilinear Factor AnalyzersCode0
The Impact of Element Ordering on LM Agent PerformanceCode0
Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block ModelsCode0
Premise selection with neural networks and distributed representation of featuresCode0
Scaling Gaussian Process Regression with DerivativesCode0
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentationCode0
XNB: Explainable Class-Specific NaIve-Bayes ClassifierCode0
Preventing Model Collapse in Gaussian Process Latent Variable ModelsCode0
Multi-Perspective, Simultaneous EmbeddingCode0
Earthmover-based manifold learning for analyzing molecular conformation spacesCode0
ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning ProblemsCode0
Deep Random Splines for Point Process Intensity Estimation of Neural Population DataCode0
ECG Beats Fast Classification Base on Sparse DictionariesCode0
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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