This article is cited in 3 papers

Dimensionality reduction for mixture of probabilistic principal component analyzers in relation to the tasks of medical diagnostics

M. P. Krivenko

Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The article considers algorithms of choosing structural parameters characterizing the mixture of probabilistic principal component analyzers model in relation to the tasks of medical diagnostics. The solution is proposed to search by combining the application of information criteria for the formation of initial approximations, followed by refinement of the resulting solutions. The described approaches and algorithms lead to results that generally do not guarantee the best solution. But they make it possible to clarify whether it is possible to reduce the dimensionality which leads to an increase in the quality of classification. In addition, new information about the objects of study is being formed. Using the example of experiments to diagnose liver diseases and predict the chemical composition of urinary stones, the capabilities of the described data analysis procedures are demonstrated. The proposed solutions are a source of improving the accuracy of classification and give impetus to experts in the subject area to clarify the essence of the processes.

Keywords: principal components analysis, Gaussian mixture model, dimensionality reduction, information criterions, cross-validation, medical diagnostics.

Received: 16.07.2019

DOI: 10.14357/08696527190401