SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 741–750 of 3304 papers

TitleStatusHype
A Wasserstein perspective of Vanilla GANs—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—0
Text Clustering with Large Language Model Embeddings—0
Curvature Augmented Manifold Embedding and LearningCode0
Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings—0
Temporally-Consistent Koopman Autoencoders for Forecasting Dynamical Systems—0
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning—0
Enhancing IoT Security Against DDoS Attacks through Federated Learning—0
Randomized Principal Component Analysis for Hyperspectral Image Classification—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