Skip to Main content Skip to Navigation
Journal articles

Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering.

Document type :
Journal articles
Complete list of metadata

https://hal-centralesupelec.archives-ouvertes.fr/hal-03521350
Contributor : Florent Bouchard Connect in order to contact the contributor
Submitted on : Tuesday, January 11, 2022 - 3:09:42 PM
Last modification on : Monday, January 24, 2022 - 9:08:52 AM

File

TSP_robust_Grassmann.pdf
Files produced by the author(s)

Identifiers

Citation

Antoine Collas, Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac, Chengfang Ren, et al.. Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering.. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2021, 69, pp.6546-6560. ⟨10.1109/TSP.2021.3130997⟩. ⟨hal-03521350⟩

Share

Metrics

Les métriques sont temporairement indisponibles