Abstract
Machine learning builds mathematical models from data containing multiple attributes (i.e., variables) in order to predict some variables from others. For example, in a cancer prediction application, each data point might contain the variables obtained from running clinical tests, whereas the predicted variable might be a binary diagnosis of cancer. Such models are sometimes expressed as linear and nonlinear relationships between variables. These relationships are discovered in a data-driven manner by optimizing (maximizing) the “agreement” between the models and the observed data. This is an optimization problem.
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Notes
- 1.
Instead of referring to such matrices as rectangular diagonal matrices, some authors use a quotation around the word diagonal, while referring to such matrices. This is because the word “diagonal” was originally reserved for square matrices.
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Aggarwal, C.C. (2020). Linear Algebra and Optimization: An Introduction. In: Linear Algebra and Optimization for Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-40344-7_1
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DOI: https://doi.org/10.1007/978-3-030-40344-7_1
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