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 1151–1175 of 3304 papers

TitleStatusHype
A Low Effort Approach to Structured CNN Design Using PCA—0
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images—0
Deep Sufficient Representation Learning via Mutual Information—0
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection—0
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks—0
DeepRT: deep learning for peptide retention time prediction in proteomics—0
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective—0
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets—0
A study of the classification of low-dimensional data with supervised manifold learning—0
Adaptive Randomized Dimension Reduction on Massive Data—0
Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients—0
Generalizing Correspondence Analysis for Applications in Machine Learning—0
A study of semantic augmentation of word embeddings for extractive summarization—0
Deep neural networks for the evaluation and design of photonic devices—0
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—0
Deep Monocular Visual Odometry for Ground Vehicle—0
Deep matrix factorizations—0
AstroM^3: A self-supervised multimodal model for astronomy—0
Deep Manifold Transformation for Nonlinear Dimensionality Reduction—0
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness—0
Asteroids co-orbital motion classification based on Machine Learning—0
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—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