SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 621–630 of 3304 papers

TitleStatusHype
Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys—0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis—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
Continuum directions for supervised dimension reduction—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