Eigenvectors

Eigenvectors are vectors in a multidimensional space that are orthogonal to all other vectors in the space, and that point in the direction of maximum variance. They are important building blocks in linear algebra, used in many fields such as machine learning, computer science, and data analysis. Eigenvectors can be used to compute eigenvalues, which are used to describe the distribution of data, or the directional distribution of data, in a multidimensional space. Eigenvectors are also important for the detection of patterns in data, and for the identification of outliers. They are important for understanding the underlying structure of any dataset and for the analysis of complex systems.