SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1926–1950 of 3304 papers

TitleStatusHype
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks—0
Survey: Geometric Foundations of Data Reduction—0
Physical Action Categorization using Signal Analysis and Machine Learning—0
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion—0
Feature Clustering for Support Identification in Extreme Regions—0
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection—0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Review of Swarm Intelligence-based Feature Selection Methods—0
Modal Principal Component Analysis—0
Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model—0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization—0
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction—0
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems—0
Dimensionality Reduction for k-means Clustering—0
Scalable Derivative-Free Optimization for Nonlinear Least-Squares Problems—0
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization—0
Dimension reduction in recurrent networks by canonicalization—0
Spectral estimation from simulations via sketching—0
Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data—0
In search of the weirdest galaxies in the UniverseCode0
Numerical simulation, clustering and prediction of multi-component polymer precipitationCode0
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating TheoremCode0
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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