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 2151–2175 of 3304 papers

TitleStatusHype
Structured Feature Selection—0
Study on Downlink CSI compression: Are Neural Networks the Only Solution?—0
Subject-independent Classification of Meditative State from the Resting State using EEG—0
Subspace approximation with outliers—0
Subspace clustering of dimensionality-reduced data—0
Subspace Embeddings for the Polynomial Kernel—0
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection—0
Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates—0
Sufficient Dimension Reduction for Average Causal Effect Estimation—0
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey—0
Sufficient Forecasting Using Factor Models—0
Super-Bit Locality-Sensitive Hashing—0
Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information—0
Supervised Dimensionality Reduction and Visualization using Centroid-encoder—0
Supervised Dimensionality Reduction via Distance Correlation Maximization—0
Supervised Exponential Family Principal Component Analysis via Convex Optimization—0
Supervised Kernel PCA For Longitudinal Data—0
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks—0
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons—0
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities—0
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks—0
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction—0
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering—0
Supervised Visualization for Data Exploration—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