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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 851–875 of 3304 papers

TitleStatusHype
An Analysis of the t-SNE Algorithm for Data Visualization—0
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
Exact Cluster Recovery via Classical Multidimensional Scaling—0
Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation—0
BDEC:Brain Deep Embedded Clustering model—0
Analyzing movies to predict their commercial viability for producers—0
A Discussion On the Validity of Manifold Learning—0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design—0
Bayesian nonparametric Principal Component Analysis—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
A dimensionality reduction technique based on the Gromov-Wasserstein distance—0
Bayesian neural networks and dimensionality reduction—0
Bayesian model and dimension reduction for uncertainty propagation: applications in random media—0
Bayesian Learning of Parameterised Quantum Circuits—0
Analytic Bilinear Appearance Subspace Construction for Modeling Image Irradiance under Natural Illumination and Non-Lambertian Reflectance—0
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence—0
A cognitive based Intrusion detection system—0
Bayesian Learning of Dynamic Multilayer Networks—0
Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics—0
Bayesian inverse regression for dimension reduction with small datasets—0
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces—0
Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—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