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 751–800 of 3304 papers

TitleStatusHype
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—0
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
Decoding Emotional Experience through Physiological Signal Processing—0
Decoding Imagined Speech and Computer Control using Brain Waves—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images—0
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data—0
An Information Theoretic Feature Selection Framework for Big Data under Apache Spark—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Deep Compressed Learning for 3D Seismic Inversion—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
A review of unsupervised learning in astronomy—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
Deep Embedding Kernel—0
Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks—0
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion—0
Deep Features for training Support Vector Machine—0
A Robust Approach for Securing Audio Classification Against Adversarial Attacks—0
Deep Gaussian Mixture Models—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
A selective review of sufficient dimension reduction for multivariate response regression—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
A Fully Convolutional Network for MR Fingerprinting—0
A Semiparametric Approach to Interpretable Machine Learning—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods—0
Estimation of a function of low local dimensionality by deep neural networks—0
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures—0
Deep Learning Architecture for Motor Imaged Words—0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—0
Deep Learning for Causal Inference—0
Deep Learning for Efficient GWAS Feature Selection—0
Detection and Evaluation of Clusters within Sequential Data—0
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—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
An Incremental Non-Linear Manifold Approximation Method—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