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 2901–2950 of 3304 papers

TitleStatusHype
Classic machine learning methods—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Classification of Cervical Cancer Dataset—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Classification with Repulsion Tensors: A Case Study on Face Recognition—0
Class Mean Vector Component and Discriminant Analysis—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Distance preserving model order reduction of graph-Laplacians and cluster analysis—0
Clustering small datasets in high-dimension by random projection—0
Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data—0
ClusTop: An unsupervised and integrated text clustering and topic extraction framework—0
C(NN)FD -- Deep Learning Modelling of Multi-Stage Axial Compressors Aerodynamics—0
Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics—0
Cognitive Coding of Speech—0
Collaborative causal inference on distributed data—0
Collaborative Gaussian Processes for Preference Learning—0
Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys—0
Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering—0
Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins—0
Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset—0
Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine—0
Communication-efficient k-Means for Edge-based Machine Learning—0
Compact and Effective Representations for Sketch-based Image Retrieval—0
Compact Learning for Multi-Label Classification—0
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Company2Vec -- German Company Embeddings based on Corporate Websites—0
Company classification using machine learning—0
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks—0
Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications—0
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction—0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Comparing of Term Clustering Frameworks for Modular Ontology Learning—0
Comparing Similarity Measures for Distributional Thesauri—0
Comparison among dimensionality reduction techniques based on Random Projection for cancer classification—0
Comparison of feature extraction and dimensionality reduction methods for single channel extracellular spike sorting—0
Comparison of Machine Learning Models in Food Authentication Studies—0
Comparison of Methods in Skin Pigment Decomposition—0
Comprehensive OOD Detection Improvements—0
A comprehensive survey on computational learning methods for analysis of gene expression data—0
Compressed Dictionary Learning—0
Compressed Subspace Learning Based on Canonical Angle Preserving Property—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