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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 1526–1550 of 3304 papers

TitleStatusHype
Persuasion by Dimension Reduction—0
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations—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
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance—0
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction—0
Domain Generalization via Domain-based Covariance Minimization—0
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
On the Correspondence between Gaussian Processes and Geometric Harmonics—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Efficient GPU implementation of randomized SVD and its applications—0
DenDrift: A Drift-Aware Algorithm for Host Profiling—0
Robust Linear Classification from Limited Training Data—0
Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design—0
Dimension Reduction for Fréchet Regression—0
Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction—0
Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers—0
Learning Stochastic Representations of Physical Systems—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