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 1426–1450 of 3304 papers

TitleStatusHype
Robust Linear Classification from Limited Training Data—0
Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design—0
DenDrift: A Drift-Aware Algorithm for Host Profiling—0
Dimension Reduction for Fréchet Regression—0
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers—0
Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Learning Stochastic Representations of Physical Systems—0
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis—0
SpaceMAP: Visualizing Any Data in 2-dimension by Space Expansion—0
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—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
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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