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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 1276–1300 of 3304 papers

TitleStatusHype
Dimensionality Reduction and Prioritized Exploration for Policy Search—0
Bayesian Calibration for Activity Based Models—0
Diffusion Maps : Using the Semigroup Property for Parameter Tuning—0
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed DataCode1
Uniform Approximations for Randomized Hadamard Transforms with Applications—0
Are Latent Factor Regression and Sparse Regression Adequate?—0
Split Semantic Detection in Sandplay Images—0
On genetic programming representations and fitness functions for interpretable dimensionality reductionCode0
On the Robustness of CountSketch to Adaptive Inputs—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data—0
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains—0
Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction and Multisensory Unsupervised Cause-effect Learning—0
Exploring the Unfairness of DP-SGD Across Settings—0
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence—0
Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering—0
Thermal hand image segmentation for biometric recognition—0
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis—0
Schrödinger Risk Diversification Portfolio—0
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Incorporating Texture Information into Dimensionality Reduction for High-Dimensional ImagesCode0
Using the left Gram matrix to cluster high dimensional data—0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks—0
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods—0
Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data—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