SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 451475 of 3304 papers

TitleStatusHype
Instance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace LearningCode0
Auto-Encoding Variational Bayes for Inferring Topics and VisualizationCode0
Integrative Factorization of Bidimensionally Linked MatricesCode0
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parametersCode0
Degradation Modeling and Prognostic Analysis Under Unknown Failure ModesCode0
Interpretable dimensionality reduction using weighted linear transformationCode0
Interpretable non-linear dimensionality reduction using gaussian weighted linear transformationCode0
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG DataCode0
Automatic Differentiation in PyTorchCode0
Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective OptimizationCode0
Introducing user-prescribed constraints in Markov chains for nonlinear dimensionality reductionCode0
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)Code0
Detecting covariate drift in text data using document embeddings and dimensionality reductionCode0
Deep Temporal Clustering: Fully unsupervised learning of time-domain featuresCode0
Optimal Projections for Discriminative Dictionary Learning using the JL-lemmaCode0
Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial SystemsCode0
A Deep Learning Framework for Assessing Physical Rehabilitation ExercisesCode0
Joint Embedding of GraphsCode0
A novel approach for Fair Principal Component Analysis based on eigendecompositionCode0
Autonomous skill discovery with Quality-Diversity and Unsupervised DescriptorsCode0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying densityCode0
Learning Embeddings into Entropic Wasserstein SpacesCode0
Language Model Training Paradigms for Clinical Feature EmbeddingsCode0
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified