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 2001–2025 of 3304 papers

TitleStatusHype
Histograms of Sparse Codes for Object Detection—0
History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterization—0
Hodge Laplacians and Hodge Diffusion Maps—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
Horizontal and Vertical Attention in Transformers—0
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?—0
How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?—0
How Do People Differ? A Social Media Approach—0
How to Use less Features and Reach Better Performance in Author Gender Identification—0
Human Activity Recognition using Smartphone—0
Human Activity Recognition using Smartphones—0
Human brain activity for machine attention—0
Human Face Recognition using Line Features—0
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy—0
Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering—0
Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images—0
Hybrid Kronecker Product Decomposition and Approximation—0
Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models—0
Hybrid quantum-classical classifier based on tensor network and variational quantum circuit—0
Hybrid Quantum-Classical Machine Learning for Sentiment Analysis—0
Hybrid Subspace Learning for High-Dimensional Data—0
Hybrid Two-Stage Reconstruction of Multiscale Subsurface Flow with Physics-informed Residual Connected Neural Operator—0
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation—0
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—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