SOTAVerified

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 1501–1525 of 3304 papers

TitleStatusHype
Three-body renormalization group limit cycles based on unsupervised feature learning—0
ELBD: Efficient score algorithm for feature selection on latent variables of VAE—0
Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial SystemsCode0
A Data Quarantine Model to Secure Data in Edge Computing—0
Efficient Binary Embedding of Categorical Data using BinSketch—0
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape DescriptorCode0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
Active Linear Regression for _p Norms and Beyond—0
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
ExClus: Explainable Clustering on Low-dimensional Data Representations—0
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting—0
Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization—0
Data-driven Uncertainty Quantification in Computational Human Head Models—0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Adaptive Weighted Multi-View Clustering—0
Merging Two Cultures: Deep and Statistical Learning—0
Autonomous Dimension Reduction by Flattening Deformation of Data Manifold under an Intrinsic Deforming Field—0
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
Computational Graph Completion—0
A Dimensionality Reduction Approach for Convolutional Neural Networks—0
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey—0
Dimensionality Reduction for Wasserstein Barycenter—0
Show:102550
← PrevPage 61 of 133Next →
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