SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 481–490 of 3304 papers

TitleStatusHype
Generative adversarial learning with optimal input dimension and its adaptive generator architecture—0
Distributional Reference Class Forecasting of Corporate Sales Growth With Multiple Reference Variables—0
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data—0
Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey—0
GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning—0
Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanismsCode0
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
QUACK: Quantum Aligned Centroid KernelCode0
Utilizing Machine Learning and 3D Neuroimaging to Predict Hearing Loss: A Comparative Analysis of Dimensionality Reduction and Regression Techniques—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