Semisupervised learning from dissimilarity data.
Comput Stat Data Anal
; 52(10): 4643-4657, 2008 Jun 15.
Article
en En
| MEDLINE
| ID: mdl-20407600
ABSTRACT
The following two-stage approach to learning from dissimilarity data is described (1) embed both labeled and unlabeled objects in a Euclidean space; then (2) train a classifier on the labeled objects. The use of linear discriminant analysis for (2), which naturally invites the use of classical multidimensional scaling for (1), is emphasized. The choice of the dimension of the Euclidean space in (1) is a model selection problem; too few or too many dimensions can degrade classifier performance. The question of how the inclusion of unlabeled objects in (1) affects classifier performance is investigated. In the case of spherical covariances, including unlabeled objects in (1) is demonstrably superior. Several examples are presented.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Comput Stat Data Anal
Año:
2008
Tipo del documento:
Article
País de afiliación:
Estados Unidos