SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2051–2075 of 3304 papers

TitleStatusHype
Learned SVD: solving inverse problems via hybrid autoencoding—0
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender SystemsCode0
Bounded Manifold Completion—0
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection—0
Projection Pursuit with Applications to scRNA Sequencing Data—0
MM Algorithms for Distance Covariance based Sufficient Dimension Reduction and Sufficient Variable Selection—0
From deep learning to mechanistic understanding in neuroscience: the structure of retinal predictionCode0
Discriminative Dimension Reduction based on Mutual Information—0
The Wasserstein-Fourier Distance for Stationary Time SeriesCode0
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Self Organizing Nebulous Growths for Robust and Incremental Data VisualizationCode0
Control of ecological outcomes through deliberate parameter changes in a model of the gut microbiomeCode0
Hybrid Kronecker Product Decomposition and Approximation—0
Using Dimensionality Reduction to Optimize t-SNECode0
On Distance and Kernel Measures of Conditional Independence—0
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels—0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Precision-Recall Balanced Topic Modelling—0
Dimensionality reduction: theoretical perspective on practical measures—0
Solving Interpretable Kernel Dimensionality Reduction—0
Distributed estimation of principal support vector machines for sufficient dimension reduction—0
Logical Interpretations of Autoencoders—0
Matrix Normal PCA for Interpretable Dimension Reduction and Graphical Noise Modeling—0
Kernelized Multiview Subspace Analysis by Self-weighted Learning—0
GRASPEL: Graph Spectral Learning at Scale—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