Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions
Dimensionality reduction (DR) simplifies complex data from genomics, imaging, sensors, and language into interpretable forms that support visualization, clustering, and modeling. Yet widely used methods like principal component analysis, t-distributed stochastic neighbor embedding, uniform manifold...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-07-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-3025.pdf |
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