SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 471–480 of 3304 papers

TitleStatusHype
Input Guided Multiple Deconstruction Single Reconstruction neural network models for Matrix Factorization—0
Automated Anomaly Detection on European XFEL Klystrons—0
Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging—0
An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Lens functions for exploring UMAP Projections with Domain KnowledgeCode0
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured SystemsCode0
Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits—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