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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 1326–1350 of 3304 papers

TitleStatusHype
Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection—0
2D+3D facial expression recognition via embedded tensor manifold regularization—0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models—0
Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings—0
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey—0
Error-Correcting Neural Networks for Two-Dimensional Curvature Computation in the Level-Set MethodCode0
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
Knowledge Base Index Compression via Dimensionality and Precision Reduction—0
Encoding large information structures in linear algebra and statistical modelsCode0
Systematic analysis reveals key microRNAs as diagnostic and prognostic factors in progressive stages of lung cancer—0
An efficient aggregation method for the symbolic representation of temporal dataCode1
SLISEMAP: Supervised dimensionality reduction through local explanationsCode1
Feature Space Hijacking Attacks against Differentially Private Split Learning—0
An Introduction to Autoencoders—0
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication—0
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra—0
Machine-Learning the Classification of Spacetimes—0
Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOMCode1
Modelling matrix time series via a tensor CP-decomposition—0
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Exact Post-selection Inference For Tracking S&P500—0
Uniform-in-Phase-Space Data Selection with Iterative Normalizing FlowsCode0
Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction—0
Manifold learning via quantum dynamics—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