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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 of 3304 papers

TitleStatusHype
Randomized Principal Component Analysis for Hyperspectral Image Classification—0
Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy—0
Efficiently Computing Similarities to Private Datasets—0
Signed graphs in data sciences via communicability geometry—0
A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection—0
Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)—0
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering—0
tLaSDI: Thermodynamics-informed latent space dynamics identification—0
DiffRed: Dimensionality Reduction guided by stable rankCode0
An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization—0
A mechanism-driven reinforcement learning framework for shape optimization of airfoils—0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Explaining Genetic Programming Trees using Large Language Models—0
Black-Box k-to-1-PCA Reductions: Theory and Applications—0
Density-based Isometric Mapping—0
Permutation invariant functions: statistical tests, density estimation, and computationally efficient embedding—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
Γ-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data—0
ELA: Efficient Local Attention for Deep Convolutional Neural Networks—0
Dimensionality reduction techniques to support insider trading detection—0
Policy Optimization for PDE Control with a Warm Start—0
Derivative-enhanced Deep Operator NetworkCode0
Degradation Modeling and Prognostic Analysis Under Unknown Failure ModesCode0
Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures—0
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs—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