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 2201–2225 of 3304 papers

TitleStatusHype
Learning Clustered Representation for Complex Free Energy Landscapes—0
Learning Collective Behaviors from Observation—0
Learning Deep Representations By Distributed Random Samplings—0
Learning Deep Representation Without Parameter Inference for Nonlinear Dimensionality Reduction—0
Learning Densities Conditional on Many Interacting Features—0
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows—0
Learning Entropic Wasserstein Embeddings—0
Learning Environment Models with Continuous Stochastic Dynamics—0
An unsupervised approach to Geographical Knowledge Discovery using street level and street network images—0
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction—0
Learning Image Derived PDE-Phenotypes from fMRI Data—0
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems—0
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds—0
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions—0
Learning Isometric Embeddings of Road Networks using Multidimensional Scaling—0
Learning Locality-Constrained Collaborative Representation for Face Recognition—0
Learning low-dimensional dynamics from whole-brain data improves task capture—0
Learning Low-Dimensional Temporal Representations—0
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data—0
Learning Manifolds from Non-stationary Streaming Data—0
Learning Mixtures of Arbitrary Distributions over Large Discrete Domains—0
Learning Multiple Non-linear Sub-spaces Using K-RBMs—0
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains—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