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 1326–1350 of 3304 papers

TitleStatusHype
Convolutional Autoencoders for Reduced-Order Modeling—0
Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction—0
Application Research On Real-Time Perception Of Device Performance Status—0
A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla word Recognition—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—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
Convergence rates of Kernel Conjugate Gradient for random design regression—0
Application of Fuzzy Clustering for Text Data Dimensionality Reduction—0
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification—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
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
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA—0
A Critical Note on the Evaluation of Clustering Algorithms—0
A Category Space Approach to Supervised Dimensionality Reduction—0
Contrastive inverse regression for dimension reduction—0
Contrastive analysis for scatterplot-based representations of dimensionality reduction—0
Application of Deep Learning for Predictive Maintenance of Oilfield Equipment—0
Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models—0
Continuum directions for supervised dimension reduction—0
Application of Computer Vision Techniques for Segregation of PlasticWaste based on Resin Identification Code—0
Contextual Categorization Enhancement through LLMs Latent-Space—0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis—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