SOTAVerified

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 2676–2700 of 3304 papers

TitleStatusHype
Learning Neural Representations of Human Cognition across Many fMRI StudiesCode0
Stochastic variance reduced multiplicative update for nonnegative matrix factorization—0
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics—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
Deep Triphone Embedding Improves Phoneme Recognition—0
Elliptical modeling and pattern analysis for perturbation models and classfication—0
Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings—0
S-Isomap++: Multi Manifold Learning from Streaming Data—0
Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model—0
Fair Kernel Learning—0
VAMPnets: Deep learning of molecular kineticsCode0
Information-Theoretic Representation Learning for Positive-Unlabeled Classification—0
Regression-aware decompositions—0
Dimensionality Reduction Ensembles—0
Mechanisms of dimensionality reduction and decorrelation in deep neural networks—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping—0
Sparse Exact PGA on Riemannian Manifolds—0
Influ\^encia de T\'ecnicas N\~ao-supervisionadas de Redu \~ao de Dimensionalidade para Organiza \~ao Flex\' de Documentos (The Unsupervised Dimensionality Reduction Weight on Flexible Document Organization)[In Portuguese]—0
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case—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