Basic operators and adjoints |
A more heterogeneous example of a vector space is data tracks. A depth-sounding survey of a lake can make a vector space that is a collection of tracks, a vector of vectors (each vector having a different number of components, because lakes are not square). This vector space of depths along tracks in a lake contains the depth values only. The -coordinate information locating each measured depth value is (normally) something outside the vector space. A data space could also be a collection of echo soundings, waveforms recorded along tracks.
We briefly recall information about vector spaces found in elementary books: Let be any scalar. Then, if is a vector and is conformable with it, then other vectors are and . The size measure of a vector is a positive value called a norm. The norm is usually defined to be the dot product (also called the norm), say . For complex data it is , where is the complex conjugate of . A notation that does transpose and complex conjugate at the same time is . In theoretical work, the ``size of a vector'' means the vector's norm. In computational work the ``size of a vector'' means the number of components in the vector.
Norms generally include a weighting function. In physics, the norm generally measures a conserved quantity like energy or momentum; therefore, for example, a weighting function for magnetic flux is permittivity. In data analysis, the proper choice of the weighting function is a practical statistical issue, discussed repeatedly throughout this book. The algebraic view of a weighting function is that it is a diagonal matrix with positive values spread along the diagonal, and it is denoted . With this weighting function, the norm of a data space is denoted . Standard notation for norms uses a double absolute value, where . A central concept with norms is the triangle inequality, a proof you might recall (or reproduce with the use of dot products).
Basic operators and adjoints |