SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 1411–1420 of 3304 papers

TitleStatusHype
Split Semantic Detection in Sandplay Images—0
Are Latent Factor Regression and Sparse Regression Adequate?—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
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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