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quadratic forms

Orthogonal projections of multidimensional ellipsoids – IV – the relation to a Schur complement of the quadratic form matrix

The first three posts in this series showed that the orthogonal projection of a (n-1)-dimensional ellipsoid from a n-dimensional Euclidean space to a (n-1)-dimensional subspace has a surface which is a (n-2)-dimensional ellipsoid. In this fourth post we will extend our insights to projections down onto a p-dimensional sub-spaces with 1 ≤ p < n. The sub-space has a (n-p)-dimensional… Read More »Orthogonal projections of multidimensional ellipsoids – IV – the relation to a Schur complement of the quadratic form matrix

Ellipses constructed from elements of a matrix defining a quadratic form

Ellipses via matrix elements – II – numerical tests of formulas

During the last posts, I have discussed properties of ellipses and ways to (re-) construct them from elements of a symmetric, invertible and positive-definite (2×2)-matrix, which defines a quadratic form. In the context of Machine Learning we often have to determine confidence ellipses from elements of a numerically determined variance-covariance matrix of statistical bivariate vector-distributions. Formulas relating the geometric properties… Read More »Ellipses via matrix elements – II – numerical tests of formulas

Ellipses determined from a matrix mediating a quadratic form

Ellipses via matrix elements – I – basic derivations and formulas

Ellipses are specific two-dimensional geometrical objects. They are of interest in many contexts – e.g. in physics, engineering and in cryptography. However, they also appear in statistics. For example, in the form of elliptic contour lines of Bivariate Normal Distributions [BVDs] and as elliptic contours of the projections of Multivariate Normal Distributions [MVDs] onto coordinate planes. Approximate BVDs/MVDs in turn… Read More »Ellipses via matrix elements – I – basic derivations and formulas