SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2381–2390 of 3304 papers

TitleStatusHype
Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data—0
Using PCA to Efficiently Represent State Spaces—0
Using pseudo-senses for improving the extraction of synonyms from word embeddings—0
Using the left Gram matrix to cluster high dimensional data—0
Using Topological Data Analysis to classify Encrypted Bits—0
UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation—0
Utilizing Machine Learning and 3D Neuroimaging to Predict Hearing Loss: A Comparative Analysis of Dimensionality Reduction and Regression Techniques—0
Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL—0
VAE-KRnet and its applications to variational Bayes—0
Shuttle Between the Instructions and the Parameters of Large Language Models—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