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Revista Cubana de Ciencias Informáticas

versão On-line ISSN 2227-1899

Resumo

PEREZ VERONA, Isabel Cristina  e  ARCO GARCIA, Leticia. A brief review on unsupervised metric learning. Rev cuba cienc informat [online]. 2016, vol.10, n.4, pp.43-67. ISSN 2227-1899.

ABSTRACT Several machine learning methods rely on the notion of distances in a multidimensional space, these distances are used for estimating the similarity between two objects, according to historical data. In such cases, when the metric is specifically designed to the context, better results are often obtained. However, designing a metric is a complex task. Metric learning automatically learns a distance metric according to the characteristics of the data. Unsupervised metric learning algorithms have achieved good results in cases where there is not available much information about the data. These algorithms do not require class label information, they are applied to improve unsupervised machine learning methods, mainly for improving clustering results. Here we will mention some of the recent works done in this area, their advantages, disadvantages and applications.

Palavras-chave : unsupervised metric learning; dimensionality reduction; distance.

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