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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 1301–1325 of 3304 papers

TitleStatusHype
Feature Space Hijacking Attacks against Differentially Private Split Learning—0
Feature Space Sketching for Logistic Regression—0
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations—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
Functional sufficient dimension reduction through information maximization with application to classification—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Computational Graph Completion—0
Gauge-optimal approximate learning for small data classification problems—0
Efficiently Computing Similarities to Private Datasets—0
Computational Techniques in Multispectral Image Processing: Application to the Syriac Galen Palimpsest—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
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images—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
Efficient Learning and Planning with Compressed Predictive States—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