SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 3051–3100 of 3304 papers

TitleStatusHype
Deep Learning for Efficient GWAS Feature Selection—0
Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling—0
Deep Learning for Size and Microscope Feature Extraction and Classification in Oral Cancer: Enhanced Convolution Neural Network—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management—0
Deep Learning Multidimensional Projections—0
Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification—0
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness—0
Deep Manifold Transformation for Nonlinear Dimensionality Reduction—0
Deep matrix factorizations—0
Deep Monocular Visual Odometry for Ground Vehicle—0
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows—0
Deep neural networks for the evaluation and design of photonic devices—0
Generalizing Correspondence Analysis for Applications in Machine Learning—0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets—0
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective—0
DeepRT: deep learning for peptide retention time prediction in proteomics—0
Deep Sufficient Representation Learning via Mutual Information—0
Deep topic modeling by multilayer bootstrap network and lasso—0
Deep Triphone Embedding Improves Phoneme Recognition—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
Deep Variational Sufficient Dimensionality Reduction—0
Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
Demixed Principal Component Analysis—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
Demystifying Embedding Spaces using Large Language Models—0
DenDrift: A Drift-Aware Algorithm for Host Profiling—0
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI—0
Density-based Isometric Mapping—0
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems—0
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification—0
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features—0
Detection and Evaluation of Clusters within Sequential Data—0
Detection and Identification Accuracy of PCA-Accelerated Real-Time Processing of Hyperspectral Imagery—0
Detection and tracking of gas plumes in LWIR hyperspectral video sequence data—0
Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images—0
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis—0
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies—0
DG-GL: Differential geometry based geometric learning of molecular datasets—0
Diagnosing ADHD from fMRI Scans Using Hidden Markov Models—0
Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks—0
Dictionary Learning under Symmetries via Group Representations—0
DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization—0
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training—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