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Manifold Learning Theory and Applications
Abstract
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread
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Cited by
Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems
Yu, X., Shen, J.-C., Zhang, J., & Letaief, K. B. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2523903 · Cited by 1,444
What are higher-order networks?
Bick, C., Gross, E., Harrington, H. A., & Schaub, M. T. (2023) · SIAM Review · doi:10.1137/21m1414024 · Cited by 420
Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach
Bashiri, F. S., Baghaie, A., Rostami, R., Yu, Z., & D’Souza, R. M. (2018) · Journal of Imaging · doi:10.3390/jimaging5010005 · Cited by 46
CMOT: cross-modality optimal transport for multimodal inference
Alatkar, S. A., & Wang, D. (2023) · Genome biology · doi:10.1186/s13059-023-02989-8 · Cited by 24
Aghili, S. N., & Erfanian, A. (2023) · IEEE Access · doi:10.1109/access.2023.3313260 · Cited by 12
Metadata
- Venue
- CRC Press eBooks
- Publisher
- CRC Press
- Published
- 2011-12-20
- OpenAlex
- W434012021
- Cited by
- 250
- References
- 0
- FWCI
- 3.00
- Type
- book
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
