ArticlePeerJ. Computer science2025
Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction.Genome biology · 2026Article
- Genomic hallmarks of depot medroxyprogesterone acetate-associated meningiomas.Neuro-oncology · 2026Article
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Informing agent-based models with spatial data using convolutional autoencoders.Bioinformatics (Oxford, England) · 2026Article
- Evaluation of Dielectric Endurance of Nano-Additive Reinforced Polyester Composites via Hankel-RPCA Decomposition.Polymers · 2026Article
- A leakage-controlled and SHAP driven machine learning framework for paediatric respiratory disease classification using Indian hospital EHR data.BMC medical informatics and decision making · 2026Article
- DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone.BioData mining · 2026Article
- Integrating Traditional Machine Learning and Convolutional Neural Networks Into the Geometric Morphometric Pipeline for Taxonomic Classification (GM-ML-CNN).Integrative organismal biology (Oxford, England) · 2026Article
- Capacity enhancement on biosecurity for line supervisors working in commercial broiler farming Gujarat.Poultry science · 2025Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
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 approximation and projection, and autoencoders are often applied as "black boxes," neglecting interpretability, fairness, stability, and privacy. This review introduces a unified classification-linear, nonlinear, hybrid, and ensemble approaches-and assesses them against eight core challenges: dimensionality selection, overfitting, instability, noise sensitivity, bias, scalability, privacy risks, and ethical compliance. We outline solutions such as intrinsic dimensionality estimation, robust neighborhood graphs, fairness-aware embeddings, scalable algorithms, and automated tuning. Drawing on case studies from bioinformatics, vision, language, and Internet of Things analytics, we offer a practical roadmap for deploying dimensionality reduction methods that are scalable, interpretable, and ethically sound-advancing responsible artificial intelligence in high-stakes applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.