SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1271–1280 of 3304 papers

TitleStatusHype
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis—0
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks—0
Fast Robust PCA on Graphs—0
Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications—0
FAST-Splat: Fast, Ambiguity-Free Semantics Transfer in Gaussian Splatting—0
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods—0
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data—0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—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