Semisupervised learning from dissimilarity data.
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.
Trosset
Michael W
MW
Department of Statistics, Indiana University, Bloomington, IN 47405, USA.
Priebe
Carey E
CE
Park
Youngser
Y
Miller
Michael I
MI
Hum Brain Mapp. 2009 Jul;30(7):2132-41
18781592
Annu Rev Psychol. 1980;31:607-49
20812811
Psychometrika. 1965 Dec;30(4):379-93
5217606