SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1361–1370 of 3304 papers

TitleStatusHype
The Forecasting performance of the Factor model with Martingale Difference errors—0
Spatial Transcriptomics Dimensionality Reduction using Wavelet BasesCode0
Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks—0
Revisiting Classical Multiclass Linear Discriminant Analysis with a Novel Prototype-based Interpretable Solution—0
LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
Drone Flocking Optimization using NSGA-II and Principal Component Analysis—0
Novel optimized crow search algorithm for feature selectionCode0
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal ParticlesCode0
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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