SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1376–1400 of 3304 papers

TitleStatusHype
Feature selection or extraction decision process for clustering using PCA and FRSD—0
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record DataCode0
Gaussian Determinantal Processes: a new model for directionality in data—0
Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial SystemsCode0
ELBD: Efficient score algorithm for feature selection on latent variables of VAE—0
A Data Quarantine Model to Secure Data in Edge Computing—0
Three-body renormalization group limit cycles based on unsupervised feature learning—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
Efficient Binary Embedding of Categorical Data using BinSketch—0
Active Linear Regression for _p Norms and Beyond—0
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
ExClus: Explainable Clustering on Low-dimensional Data Representations—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—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
The chemical space of terpenes: insights from data science and AICode1
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
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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