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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1826–1850 of 3304 papers

TitleStatusHype
Feature selection in functional data classification with recursive maxima hunting—0
Feature selection or extraction decision process for clustering using PCA and FRSD—0
Feature Selection Using Reinforcement Learning—0
Feature Space Hijacking Attacks against Differentially Private Split Learning—0
Feature Space Sketching for Logistic Regression—0
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data—0
Privacy-Preserving Federated Deep Clustering based on GAN—0
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis—0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations—0
Federated Multilinear Principal Component Analysis with Applications in Prognostics—0
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Finding Real-World Orbital Motion Laws from Data—0
Finding Rule-Interpretable Non-Negative Data Representation—0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic and Network Heterogeneity—0
Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model—0
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces—0
First-order bifurcation detection for dynamic complex networks—0
Fitting a Simplicial Complex using a Variation of k-means—0
Flashlight Search Medial Axis: A Pixel-Free Pore-Network Extraction Algorithm—0
Flexible sampling of discrete data correlations without the marginal distributions—0
Forecastable Component Analysis (ForeCA)—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