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 2451–2475 of 3304 papers

TitleStatusHype
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets—0
Perceptual Visual Interactive Learning—0
Modified Multidimensional Scaling and High Dimensional Clustering—0
Optimal terminal dimensionality reduction in Euclidean space—0
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning—0
Fast Randomized PCA for Sparse DataCode0
A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis—0
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions—0
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable ModelsCode0
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation ApproachCode0
ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation—0
Asymptotics for Sketching in Least Squares RegressionCode0
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring SignalsCode0
Learning Optimal Deep Projection of ^18F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes—0
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras—0
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations—0
Network Distance Based on Laplacian Flows on Graphs—0
Computer vision-based framework for extracting geological lineaments from optical remote sensing dataCode0
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstructionCode0
Dynamic Sparse Graph for Efficient Deep Learning—0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
Quantifying Context Overlap for Training Word Embeddings—0
Modeling Dynamics of Biological Systems with Deep Generative Neural Networks—0
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised LearningCode0
Graph filtering for data reduction and reconstruction—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