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 2751–2775 of 3304 papers

TitleStatusHype
A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations—0
A review of unsupervised learning in astronomy—0
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion—0
A Robust Approach for Securing Audio Classification Against Adversarial Attacks—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
A selective review of sufficient dimension reduction for multivariate response regression—0
A Semiparametric Approach to Interpretable Machine Learning—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction—0
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding—0
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification—0
Split Semantic Detection in Sandplay Images—0
A Stable Measure for Conditional Periodicity of Time Series using Persistent Homology—0
A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms—0
Asteroids co-orbital motion classification based on Machine Learning—0
AstroM^3: A self-supervised multimodal model for astronomy—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—0
A study of semantic augmentation of word embeddings for extractive summarization—0
A study of the classification of low-dimensional data with supervised manifold learning—0
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection—0
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images—0
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting—0
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data—0
A survey of dimensionality reduction techniques—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