Exploring unsupervised feature extraction algorithms: tackling high dimensionality in small datasets

Abstract Small datasets are common in many fields due to factors such as limited data collection opportunities or privacy concerns. These datasets often contain high-dimensional features, yet present significant challenges of dimensionality, wherein the sparsity of data in high-dimensional spaces ma...

Celý popis

Podrobná bibliografie

Hlavní autoři:	Hongqi Niu, Gabrielle B. McCallum, Anne B. Chang, Khalid Khan, Sami Azam
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Nature Portfolio 2025-07-01
Edice:	Scientific Reports
Témata:	Unsupervised High dimensionality Small datasets Feature extraction
On-line přístup:	https://doi.org/10.1038/s41598-025-07725-9
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!