Dimensionality reduction by t-Distribution adaptive manifold embedding
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04838-4.pdf
Reference31 articles.
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3. Raunak V, Gupta V, Metze F (2019) Effective dimensionality reduction for word embeddings. Proceedings of the 4th Workshop on Representation Learning for NLP 235–243
4. Mironczuk MM, Protasiewicz J (2018) A recent overview of the state-of-the-art elements of text classification. Expert Syst Appl 106:36–54
5. Dorrity MW, Saunders LM, Queitsch C, Fields S, Trapnell C (2020) Dimensionality reduction by UMAP to visualize physical and genetic interactions. Nature Commun 11(1):1537
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