The investigation is conducted in the domain of bioinformatics and aims at the classification of DNA microarrays. The next paper, Semi-Supervised Linear Discriminant Analysis through Moment-Constraint Parameter Estimation by Marco Loog, introduces a variant of a semi-supervised linear discriminant function. The approach does not rely on arbitrary assumptions on the information mutually conveyed by labeled and unlabeled data that a diffusion graph based on Euclidean distance captures the proper relationships among the data. The meta-algorithm, which proceeds by finding prototypes from the labeled data and then clusters the remaining data iteratively, reduces the overall computational complexity of the procedure w.r.t. standard methods and may be instantiated with a variety of different kernel-based clustering techniques.
F., S., Trentin, E. (2014). Partially supervised learning for pattern recognition. PATTERN RECOGNITION LETTERS, 37(special issue), 1-3 [10.1016/j.patrec.2013.10.014].
Partially supervised learning for pattern recognition
TRENTIN, EDMONDO
2014-01-01
Abstract
The investigation is conducted in the domain of bioinformatics and aims at the classification of DNA microarrays. The next paper, Semi-Supervised Linear Discriminant Analysis through Moment-Constraint Parameter Estimation by Marco Loog, introduces a variant of a semi-supervised linear discriminant function. The approach does not rely on arbitrary assumptions on the information mutually conveyed by labeled and unlabeled data that a diffusion graph based on Euclidean distance captures the proper relationships among the data. The meta-algorithm, which proceeds by finding prototypes from the labeled data and then clusters the remaining data iteratively, reduces the overall computational complexity of the procedure w.r.t. standard methods and may be instantiated with a variety of different kernel-based clustering techniques.File | Dimensione | Formato | |
---|---|---|---|
15-SchwenkerTrentinEditorial.pdf
non disponibili
Tipologia:
PDF editoriale
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
202.47 kB
Formato
Adobe PDF
|
202.47 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/47082