SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 726–750 of 3304 papers

TitleStatusHype
DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning—0
Deep matrix factorizations—0
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Data Augmentation For Label Enhancement—0
A predictive physics-aware hybrid reduced order model for reacting flows—0
Data Dimension Reduction makes ML Algorithms efficient—0
Generalizing Correspondence Analysis for Applications in Machine Learning—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Data-Driven Forecasting of High-Dimensional Transient and Stationary Processes via Space-Time Projection—0
An Information Theoretic Feature Selection Framework for Big Data under Apache Spark—0
Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model—0
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service—0
Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace—0
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations—0
Data-driven Uncertainty Quantification in Computational Human Head Models—0
Data efficiency, dimensionality reduction, and the generalized symmetric information bottleneck—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Data-Enabled Predictive Control for Flexible Spacecraft—0
Data-independent Low-complexity KLT Approximations for Image and Video Coding—0
Deep Learning Multidimensional Projections—0
A quantitative fusion strategy of stock picking and timing based on Particle Swarm Optimized-Back Propagation Neural Network and Multivariate Gaussian-Hidden Markov Model—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—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