SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 601–650 of 3304 papers

TitleStatusHype
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
An Innovative Imputation and Classification Approach for Accurate Disease Prediction—0
A Fuzzy Approach for Feature Evaluation and Dimensionality Reduction to Improve the Quality of Web Usage Mining Results—0
Distance preserving model order reduction of graph-Laplacians and cluster analysis—0
A Functional approach for Two Way Dimension Reduction in Time Series—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures—0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—0
Cortical spatio-temporal dimensionality reduction for visual grouping—0
Clustering small datasets in high-dimension by random projection—0
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data—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
An Investigation of Newton-Sketch and Subsampled Newton Methods—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
Cost-informed dimensionality reduction for structural digital twin technologies—0
Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—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
Coupling the reduced-order model and the generative model for an importance sampling estimator—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
A Novel Approach for Intrinsic Dimension Estimation—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
Clustering based on Mixtures of Sparse Gaussian Processes—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