SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2371–2380 of 3304 papers

TitleStatusHype
Upper and Lower Bounds on the Performance of Kernel PCA—0
Upper bounds for Model-Free Row-Sparse Principal Component Analysis—0
USAAR-SHEFFIELD: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation Metrics—0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
User-friendly Foundation Model Adapters for Multivariate Time Series Classification—0
Using Deep Autoencoders for Facial Expression Recognition—0
Using Dimension Reduction to Improve the Classification of High-dimensional Data—0
Using Lexical Expansion to Learn Inference Rules from Sparse Data—0
Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems—0
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks—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