SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Showing 20512060 of 3304 papers

TitleStatusHype
Learned SVD: solving inverse problems via hybrid autoencoding0
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender SystemsCode0
Bounded Manifold Completion0
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection0
Projection Pursuit with Applications to scRNA Sequencing Data0
MM Algorithms for Distance Covariance based Sufficient Dimension Reduction and Sufficient Variable Selection0
From deep learning to mechanistic understanding in neuroscience: the structure of retinal predictionCode0
Discriminative Dimension Reduction based on Mutual Information0
The Wasserstein-Fourier Distance for Stationary Time SeriesCode0
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified