SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 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
Deep Variational Sufficient Dimensionality Reduction—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-geometric approach to feature extraction and moment reconstruction in dynamical systems—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
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
A review of unsupervised learning in astronomy—0
A Fully Convolutional Network for MR Fingerprinting—0
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?—0
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
Deep Embedding Kernel—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—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