A classification-based approach to semi-supervised clustering with pairwise constraints.
Neural Netw
; 127: 193-203, 2020 Jul.
Article
em En
| MEDLINE
| ID: mdl-32387926
ABSTRACT
In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification tasks the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method. The proposed approach is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Aprendizado de Máquina Supervisionado
Idioma:
En
Revista:
Neural Netw
Ano de publicação:
2020
Tipo de documento:
Article