SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 511–520 of 3304 papers

TitleStatusHype
Variational Bayesian Optimal Experimental Design with Normalizing Flows—0
On Computational Modeling of Sleep-Wake Cycle—0
CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference—0
Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling—0
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging—0
Human Activity Recognition using Smartphones—0
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds—0
Remote sensing framework for geological mapping via stacked autoencoders and clusteringCode1
Preventing Model Collapse in Gaussian Process Latent Variable ModelsCode0
On the reduction of Linear Parameter-Varying State-Space models—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