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 1126–1150 of 3304 papers

TitleStatusHype
Alternating Co-Quantization for Cross-Modal Hashing—0
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery—0
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI—0
DenDrift: A Drift-Aware Algorithm for Host Profiling—0
Demystifying Embedding Spaces using Large Language Models—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization—0
Demixed Principal Component Analysis—0
A Survey on Archetypal Analysis—0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing—0
Accelerating Text Mining Using Domain-Specific Stop Word Lists—0
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy—0
Compressive Mahalanobis Metric Learning Adapts to Intrinsic Dimension—0
Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data—0
A survey of dimensionality reduction techniques based on random projection—0
A survey of dimensionality reduction techniques—0
Deep Variational Sufficient Dimensionality Reduction—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data—0
Deep Triphone Embedding Improves Phoneme Recognition—0
Deep topic modeling by multilayer bootstrap network and lasso—0
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting—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