Learning in High-Dimensional Multi-response Linear Models with Quantum non-Oracular Search
DOI:
https://doi.org/10.46842/ipn.cien.v29n1a08Keywords:
linear multi-response model, multivariate analysis, quantum computing algorithmAbstract
The term “linear multi-response model” refers to a statistical model which seeks the simultaneous prediction of outcome variables from a set of predictor variables, where the relationship between each of the outcome variables with the set of predictor variables is linear.
In many real-life problems it is possible to obtain multi-response data. For this purpose, multi-response data analysis is used, whose approach addresses the simultaneous analysis of the components that make up both the resulting variables and the covariates and avoids doing component-by-component analysis, as this approach could miss making full use of the available information.
Multivariate analysis provides us with one of the most commonly used multivariate regression models, the multivariate linear model. The model of our interest is a particular case of the previous one, namely the high-dimensional, low-rank multivariate linear model, which provides us with relevant information given a sample with the use of a quantum computing algorithm, which according to the authors of the article on which the study was based, is an innovation.
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