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 1401–1425 of 3304 papers

TitleStatusHype
Computational Graph Completion—0
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey—0
A Dimensionality Reduction Approach for Convolutional Neural Networks—0
Dimensionality Reduction for Wasserstein Barycenter—0
TLDR: Twin Learning for Dimensionality ReductionCode1
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations—0
Persuasion by Dimension Reduction—0
Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model—0
SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network—0
Unsupervised Data-Driven Nuclei Segmentation For Histology Images—0
Incremental Ensemble Gaussian Processes—0
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance—0
Nonnegative spatial factorizationCode1
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction—0
Domain Generalization via Domain-based Covariance Minimization—0
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor ClassificationCode1
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Cognitive Coding of Speech—0
Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
T-SNE Is Not Optimized to Reveal Clusters in Data—0
Efficient GPU implementation of randomized SVD and its applications—0
Classification of high-dimensional data with spiked covariance matrix structure—0
On the Correspondence between Gaussian Processes and Geometric Harmonics—0
Show:102550
← PrevPage 57 of 133Next →
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