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

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...

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Bibliographic Details
Main Authors: Hongqi Niu, Gabrielle B. McCallum, Anne B. Chang, Khalid Khan, Sami Azam
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Unsupervised
High dimensionality
Small datasets
Feature extraction
Online Access:https://doi.org/10.1038/s41598-025-07725-9
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https://doi.org/10.1038/s41598-025-07725-9

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