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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 1451–1500 of 3304 papers

TitleStatusHype
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—0
Grammar-based Ordinary Differential Equation Discovery—0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation—0
Free Lunch for Gait Recognition: A Novel Relation Descriptor—0
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information—0
Fréchet Cumulative Covariance Net for Deep Nonlinear Sufficient Dimension Reduction with Random Objects—0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension—0
Graph Learning via Spectral Densification—0
Graph Neural Network Acceleration via Matrix Dimension Reduction—0
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data—0
Graphon Pooling in Graph Neural Networks—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning—0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
Graph Signal Representation with Wasserstein Barycenters—0
Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Change—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
Grassmann Averages for Scalable Robust PCA—0
Grassmann Graph Embedding—0
Fourier analysis perspective for sufficient dimension reduction problem—0
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