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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 901–925 of 3304 papers

TitleStatusHype
Ellipsoid fitting with the Cayley transformCode0
Dynamic Security Region of Natural Gas Systems in Integrated Electricity-Gas Systems—0
Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints—0
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification—0
Wireless Channel Charting: Theory, Practice, and Applications—0
A Neural Network Transformer Model for Composite Microstructure Homogenization—0
Dimensionality Reduction as Probabilistic Inference—0
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic SegmentationCode1
Online Recognition of Incomplete Gesture Data to Interface Collaborative RobotsCode0
Fast emulation of cosmological density fields based on dimensionality reduction and supervised machine-learning—0
Gradient-Free Textual Inversion—0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—0
SELFormer: Molecular Representation Learning via SELFIES Language ModelsCode1
OFTER: An Online Pipeline for Time Series Forecasting—0
Multi-Linear Kernel Regression and Imputation in Data Manifolds—0
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets—0
NeuroDAVIS: A neural network model for data visualizationCode0
Maximum Covariance Unfolding Regression: A Novel Covariate-based Manifold Learning Approach for Point Cloud Data—0
A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization—0
The G-invariant graph Laplacian—0
IMU-based Modularized Wearable Device for Human Motion Classification—0
Multimodal and multicontrast image fusion via deep generative models—0
Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon ForecastingCode0
Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis—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