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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 3276–3300 of 3304 papers

TitleStatusHype
Towards aerodynamic surrogate modeling based on β-variational autoencodersCode0
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political PartiesCode0
Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective OptimizationCode0
Parametric generation of conditional geological realizations using generative neural networksCode0
A Statistical View of Column Subset SelectionCode0
Constraint matrix factorization for space variant PSFs field restorationCode0
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed SystemsCode0
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future PerspectivesCode0
High Dimensional Bayesian Optimization via Supervised Dimension ReductionCode0
High-dimensional Bayesian optimization using low-dimensional feature spacesCode0
TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural NetworksCode0
Assessing the similarity of real matrices with arbitrary shapeCode0
High-Dimensional Feature Selection for Genomic DatasetsCode0
High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection ApproachCode0
Dimension Reduction with Prior Information for Knowledge DiscoveryCode0
Patients' Severity States Classification based on Electronic Health Record (EHR) Data using Multiple Machine Learning and Deep Learning ApproachesCode0
Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reductionCode0
Word Embedding Dimension Reduction via Weakly-Supervised Feature SelectionCode0
Measuring disentangled generative spatio-temporal representationCode0
A Computational Topology-based Spatiotemporal Analysis Technique for Honeybee AggregationCode0
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge IntelligenceCode0
Unsupervised learning with contrastive latent variable modelsCode0
PCA Reduced Gaussian Mixture Models with Applications in SuperresolutionCode0
Hillclimb-Causal Inference: A Data-Driven Approach to Identify Causal Pathways Among Parental Behaviors, Genetic Risk, and Externalizing Behaviors in ChildrenCode0
Spatial Transcriptomics Dimensionality Reduction using Wavelet BasesCode0
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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