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 1701–1725 of 3304 papers

TitleStatusHype
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Towards glass-box CNNs—0
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
Curvature-based Feature Selection with Application in Classifying Electronic Health RecordsCode1
Smile and Laugh Expressions Detection Based on Local Minimum Key Points—0
Order Embeddings from Merged Ontologies using Sketching—0
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI—0
A Linearly Convergent Algorithm for Distributed Principal Component AnalysisCode0
Analyzing movies to predict their commercial viability for producers—0
Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation—0
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey—0
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data—0
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box ModelsCode1
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Graph Neural Network Acceleration via Matrix Dimension Reduction—0
Graph Learning via Spectral Densification—0
On the Importance of Distraction-Robust Representations for Robot Learning—0
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and ClassificationCode0
A Memory Efficient Baseline for Open Domain Question AnsweringCode1
Stochastic Approximation for Online Tensorial Independent Component Analysis—0
Manifold learning with arbitrary normsCode0
A method to integrate and classify normal distributionsCode0
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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