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 2726–2750 of 3304 papers

TitleStatusHype
Application Research On Real-Time Perception Of Device Performance Status—0
Applications of machine learning to predict seasonal precipitation for East Africa—0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics—0
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images—0
Applying a random projection algorithm to optimize machine learning model for breast lesion classification—0
Applying Graph-based Keyword Extraction to Document Retrieval—0
Applying Ricci Flow to High Dimensional Manifold Learning—0
Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease—0
Approaching Metaheuristic Deep Learning Combos for Automated Data Mining—0
Approximated and User Steerable tSNE for Progressive Visual Analytics—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Approximate Matrix Multiplication with Application to Linear Embeddings—0
Approximation Algorithms for Sparse Principal Component Analysis—0
Approximation of Functions over Manifolds: A Moving Least-Squares Approach—0
A predictive physics-aware hybrid reduced order model for reacting flows—0
A Probabilistic Graph Coupling View of Dimension Reduction—0
A probabilistic view on Riemannian machine learning models for SPD matrices—0
A Process for Topic Modelling Via Word Embeddings—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—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
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning—0
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation—0
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Are Latent Factor Regression and Sparse Regression Adequate?—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