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 1776–1800 of 3304 papers

TitleStatusHype
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Towards glass-box CNNs—0
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
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
Analyzing movies to predict their commercial viability for producers—0
A Linearly Convergent Algorithm for Distributed Principal Component AnalysisCode0
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
On the Importance of Distraction-Robust Representations for Robot Learning—0
Graph Learning via Spectral Densification—0
Graph Neural Network Acceleration via Matrix Dimension Reduction—0
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
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and ClassificationCode0
Manifold learning with arbitrary normsCode0
Stochastic Approximation for Online Tensorial Independent Component Analysis—0
A method to integrate and classify normal distributionsCode0
Unsupervised Functional Data Analysis via Nonlinear Dimension ReductionCode0
Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement LearningCode0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks—0
Upper and Lower Bounds on the Performance of Kernel PCA—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