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 2301–2325 of 3304 papers

TitleStatusHype
Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data—0
Trees Assembling Mann Whitney Approach for Detecting Genome-wide Joint Association among Low Marginal Effect loci—0
Tree-structured multi-stage principal component analysis (TMPCA): theory and applications—0
ForestDSH: A Universal Hash Design for Discrete Probability Distributions—0
Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs—0
Trigonometric comparison measure: A feature selection method for text categorization—0
Triplet-Based Wireless Channel Charting: Architecture and Experiments—0
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning—0
TSLFormer: A Lightweight Transformer Model for Turkish Sign Language Recognition Using Skeletal Landmarks—0
t-SNE, Forceful Colorings and Mean Field Limits—0
T-SNE Is Not Optimized to Reveal Clusters in Data—0
Tuning-Free Disentanglement via Projection—0
Two Approaches to Supervised Image Segmentation—0
Two-sample test based on Self-Organizing Maps—0
Two-Stage Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging—0
UAEM-ITAM at SemEval-2022 Task 5: Vision-Language Approach to Recognize Misogynous Content in Memes—0
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection—0
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data—0
Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA—0
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization—0
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation—0
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process—0
Understanding Aesthetic Evaluation using Deep Learning—0
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis—0
Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principle—0
Show:102550
← PrevPage 93 of 133Next →
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