A novel autoencoder approach to feature extraction with linear separability for high-dimensional data

Feature extraction often needs to rely on sufficient information of the input data, however, the distribution of the data upon a high-dimensional space is too sparse to provide sufficient information for feature extraction. Furthermore, high dimensionality of the data also creates trouble for the se...

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Detalles Bibliográficos
Autores principales: Zheng, Jian, Qu, Hongchun, Li, Zhaoni, Li, Lin, Tang, Xiaoming, Guo, Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Data Mining and Machine Learning
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403198/
https://www.ncbi.nlm.nih.gov/pubmed/37547057
http://dx.doi.org/10.7717/peerj-cs.1061

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403198/
https://www.ncbi.nlm.nih.gov/pubmed/37547057
http://dx.doi.org/10.7717/peerj-cs.1061

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