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 991–1000 of 3304 papers

TitleStatusHype
Discriminative Dimension Reduction based on Mutual Information—0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
Diseño de un espacio semántico sobre la base de la Wikipedia. Una propuesta de análisis de la semántica latente para el idioma español—0
Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity—0
Disentangled Latent Spaces for Reduced Order Models using Deterministic Autoencoders—0
Disentangling Generative Factors of Physical Fields Using Variational Autoencoders—0
Classic machine learning methods—0
Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks—0
Disentangling Topic Models: A Cross-cultural Analysis of Personal Values through Words—0
An Impossibility Theorem for Node Embedding—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