An efficient method for clustered multi-metric learning
Abstract Distance metric learning, which aims at finding a distance metric that separates examples of one class from examples of the other classes, is the key to the success of many machine learning tasks. Although there has been an increasing interest in this field, learning a global distance metric is insufficient to obtain satisfactory results when dealing with heterogeneously distributed data. A simple solution to tackle this kind of data is based on kernel embedding methods. However, it quickly becomes computationally intractable as the number of examples increases. In this paper, we propose an efficient method that learns multiple local distance metrics instead of a single global one.…
Perspective: Essential study quality descriptors for data from nutritional epidemiologic research
Pooled analysis of secondary data increases the power of research and enables scientific discovery in nutritional epidemiology. Information on study characteristics that determine data quality is needed to enable correct reuse and interpretation of data. This study aims to define essential quality characteristics for data from observational studies in nutrition. First, a literature review was performed to get an insight on existing instruments that assess the quality of cohort, case-control, and cross-sectional studies and dietary measurement. Second, 2 face-to-face workshops were organized to determine the study characteristics that affect data quality. Third, consensus on the data descrip…