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 1951–1975 of 3304 papers

TitleStatusHype
Semi-supervised Embedding Learning for High-dimensional Bayesian OptimizationCode0
QEBA: Query-Efficient Boundary-Based Blackbox Attack—0
Physically interpretable machine learning algorithm on multidimensional non-linear fields—0
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images—0
Extração e Classificação de Características Radiômicas em Gliomas de Baixo Grau para Análise da Codeleção 1p/19q—0
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks—0
Triplet-Based Wireless Channel Charting: Architecture and Experiments—0
DartMinHash: Fast Sketching for Weighted SetsCode1
Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation—0
Adversarial Canonical Correlation AnalysisCode0
A cognitive based Intrusion detection system—0
Transformation Based Deep Anomaly Detection in Astronomical Images—0
Detection and Retrieval of Out-of-Distribution Objects in Semantic SegmentationCode1
OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach—0
Exchangeability, Conformal Prediction, and Rank Tests—0
Fast Network Embedding Enhancement via High Order Proximity ApproximationCode1
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering—0
Solving Large-Scale Sparse PCA to Certifiable (Near) OptimalityCode0
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case StudyCode0
Low-Rank Nonlinear Decoding of μ-ECoG from the Primary Auditory Cortex—0
Regularized Pooling—0
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction—0
Probing Criticality in Quantum Spin Chains with Neural Networks—0
Renormalized Mutual Information for Artificial Scientific DiscoveryCode1
On time series representations for multi-label NILMCode1
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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