SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2651–2675 of 3304 papers

TitleStatusHype
Simple strategies for recovering inner products from coarsely quantized random projections—0
Extreme Dimension Reduction for Handling Covariate Shift—0
Wisdom of the crowd from unsupervised dimension reduction—0
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery—0
Practical Hash Functions for Similarity Estimation and Dimensionality ReductionCode0
Random matrix approach to estimation of high-dimensional factor models—0
Universality of macroscopic neuronal dynamics in Caenorhabditis elegans—0
"I know it when I see it". Visualization and Intuitive Interpretability—0
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensionsCode0
Deep Gaussian Mixture Models—0
Bidirectional deep-readout echo state networksCode0
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective—0
A New Method for Performance Analysis in Nonlinear Dimensionality Reduction—0
Sparse quadratic classification rules via linear dimension reductionCode0
Evaluating deep variational autoencoders trained on pan-cancer gene expression—0
Hand Gesture Recognition with Leap Motion—0
Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM—0
Dimension Estimation Using Random Connection Models—0
Bayesian model and dimension reduction for uncertainty propagation: applications in random media—0
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations—0
From which world is your graph?—0
The neighborhood lattice for encoding partial correlations in a Hilbert spaceCode0
Approximation of Functions over Manifolds: A Moving Least-Squares Approach—0
Sampling and multilevel coarsening algorithms for fast matrix approximations—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