SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 961–970 of 3304 papers

TitleStatusHype
A framework for streamlined statistical prediction using topic models—0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Classification of Cervical Cancer Dataset—0
Dimension Reduction for time series with Variational AutoEncoders—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Dimension reduction in recurrent networks by canonicalization—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM—0
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—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