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 2001–2050 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
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification—0
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier—0
Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information—0
Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition—0
Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution—0
Hypoelliptic Diffusion Maps I: Tangent Bundles—0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)—0
I-Con: A Unifying Framework for Representation Learning—0
ICU Patient Deterioration prediction: a Data-Mining Approach—0
Identifying Chemicals Through Dimensionality Reduction—0
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data—0
Identifying Feedforward and Feedback Controllable Subspaces of Neural Population Dynamics—0
Identifying Layers Susceptible to Adversarial Attacks—0
Identifying manifolds underlying group motion in Vicsek agents—0
Identifying semantic relations in a specialized corpus through distributional analysis of a cooccurrence tensor—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
"I know it when I see it". Visualization and Intuitive Interpretability—0
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization—0
Image-based localization using LSTMs for structured feature correlation—0
Image Classification by Feature Dimension Reduction and Graph based Ranking—0
Image patch analysis and clustering of sunspots: a dimensionality reduction approach—0
Image retrieval method based on CNN and dimension reduction—0
Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—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