SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2221–2230 of 3304 papers

TitleStatusHype
Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA—0
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data—0
Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net—0
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders—0
Theoretically informed selection of latent activation in autoencoder based recommender systems—0
Theoretical Understandings of Product Embedding for E-commerce Machine Learning—0
The Perfect Marriage and Much More: Combining Dimension Reduction, Distance Measures and Covariance—0
The Potential of Quantum Techniques for Stock Price Prediction—0
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting—0
The Power of Typed Affine Decision Structures: A Case Study—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