SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1641–1650 of 3304 papers

TitleStatusHype
A canonical correlation-based framework for performance analysis of radio access networks—0
Information-Theoretic Representation Learning for Positive-Unlabeled Classification—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective—0
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression—0
Input Guided Multiple Deconstruction Single Reconstruction neural network models for Matrix Factorization—0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
Federated Multilinear Principal Component Analysis with Applications in Prognostics—0
An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns—0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations—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