SOTAVerified

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 1551–1575 of 3304 papers

TitleStatusHype
Learning Stochastic Representations of Physical Systems—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
SpaceMAP: Visualizing Any Data in 2-dimension by Space Expansion—0
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis—0
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs—0
Dimension Reduction for Data with Heterogeneous MissingnessCode0
Non-Euclidean Self-Organizing Maps—0
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?Code0
Weighted Low Rank Matrix Approximation and Acceleration—0
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in ConnectomicsCode0
Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction—0
Machine-Learned HASDM Model with Uncertainty Quantification—0
A Comparative Study of Machine Learning Methods for Predicting the Evolution of Brain Connectivity from a Baseline TimepointCode0
Disentangling Generative Factors of Physical Fields Using Variational Autoencoders—0
Concept Drift Detection in Federated Networked Systems—0
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons—0
On the use of Wasserstein metric in topological clustering of distributional data—0
Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objectiveCode0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)—0
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies—0
Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach—0
An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification—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