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
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective—0
Bidirectional deep-readout echo state networksCode0
A New Method for Performance Analysis in Nonlinear Dimensionality Reduction—0
Evaluating deep variational autoencoders trained on pan-cancer gene expression—0
Sparse quadratic classification rules via linear dimension reductionCode0
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
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations—0
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—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
Learning Neural Representations of Human Cognition across Many fMRI StudiesCode0
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics—0
Stochastic variance reduced multiplicative update for nonnegative matrix factorization—0
Dimensionality reduction methods for molecular simulations—0
Automatic Differentiation in PyTorchCode0
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network—0
DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets—0
Bayesian Inference over the Stiefel Manifold via the Givens Representation—0
SMSSVD - SubMatrix Selection Singular Value DecompositionCode0
Elliptical modeling and pattern analysis for perturbation models and classfication—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