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 676–700 of 3304 papers

TitleStatusHype
Consistent Representation Learning for High Dimensional Data Analysis—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
Construction of neural networks for realization of localized deep learning—0
Content-Aware Tweet Location Inference using Quadtree Spatial Partitioning and Jaccard-Cosine Word Embedding—0
Contextual Bandits with Sparse Data in Web setting—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
Contextual Categorization Enhancement through LLMs Latent-Space—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models—0
Contrastive analysis for scatterplot-based representations of dimensionality reduction—0
Contrastive inverse regression for dimension reduction—0
Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex—0
Application of Dictionary Learning in Alleviating Computational Burden of EEG Source Localization—0
Contrastive Multivariate Singular Spectrum Analysis—0
Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents—0
Application of Fuzzy Clustering for Text Data Dimensionality Reduction—0
Convergence rates of Kernel Conjugate Gradient for random design regression—0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs—0
Application of Symmetric Uncertainty and Mutual Information to Dimensionality Reduction and Classification of Hyperspectral Images—0
Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction—0
Convolutional Autoencoders for Reduced-Order Modeling—0
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial—0
Assessment of convolutional recurrent autoencoder network for learning wave propagation—0
Cooperative Thresholded Lasso for Sparse Linear Bandit—0
A Fully Convolutional Network for MR Fingerprinting—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