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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 1476–1500 of 3304 papers

TitleStatusHype
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering—0
Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization—0
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization—0
Foundations of Coupled Nonlinear Dimensionality Reduction—0
Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach—0
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction—0
GroupEnc: encoder with group loss for global structure preservation—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Forward-Cooperation-Backward (FCB) learning in a Multi-Encoding Uni-Decoding neural network architecture—0
Formation-Controlled Dimensionality Reduction—0
Concise Fuzzy Planar Embedding of Graphs: a Dimensionality Reduction Approach—0
Forensic Analysis of Video Files Using Metadata—0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Hand Gesture Recognition with Leap Motion—0
Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders—0
Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach—0
Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
Forecastable Component Analysis (ForeCA)—0
Concept Drift Detection in Federated Networked Systems—0
HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions—0
Flexible sampling of discrete data correlations without the marginal distributions—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