SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 626–650 of 3304 papers

TitleStatusHype
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