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- Title
Linear Algebra and Optimization in Data Analysis.
- Authors
Isihara, Paul
- Abstract
Using the framework of linear algebra and optimization as a unifying theme, a number of mathematical concepts including least-squares solutions, loss functions, covariance matrices, eigenvalues and eigenvectors, and separating hyperplanes are used to explain least-squares linear fitting, unsupervised clustering using k-means, dimensionality reduction using principal components, and binary classification of labeled data using support vector machines. To illustrate how data analysis works in practice, Python Jupyter Notebooks are used to analyze a variety of data sets connected to the city of Chicago.
- Subjects
CHICAGO (Ill.); LINEAR algebra; DATA analysis; NAIVE Bayes classification; SUPPORT vector machines; COVARIANCE matrices; NUMBER concept; EIGENVALUES; EIGENVECTORS; CLUSTER algebras
- Publication
UMAP Journal, 2023, Vol 44, Issue 2, p118
- ISSN
0197-3622
- Publication type
Article