Karhunen–Loève theorem  In the theory of stochastic processes, the Karhunen–Loève theorem (named after Kari Karhunen and Michel Loève), also known as the Kosambi–Karhunen–Loève theorem is a representation of a stochastic process as an infinite linear combination of orthogonal functions, analogous to a Fourier series representation of a function on a bounded interval. The transformation is also known as Hotelling transform and eigenvector transform, and is closely related to principal component analysis (PCA) technique widely used in image processing and in data analysis in many fields. Stochastic processes given by infinite series of this form were first considered by Damodar Dharmananda Kosambi. There exist many such expansions of a stochastic process: if the process is indexed over , any orthonormal basis of yields an expansion thereof in that form. The importance of the Karhunen–Loève theorem is that it yields the best such basis in the sense that it minimizes the total mean squared error. In contrast to a Fourier series where the coefficients are fixed numbers and the expansion basis consists of sinusoidal functions (that is, sine and cosine functions), the coefficients in the Karhunen–Loève theorem are random variables and the expansion basis depends on the process. In fact, the orthogonal basis functions used in this representation are determined by the covariance function of the process. One can think that the Karhunen–Loève transform adapts to the process in order to produce the best possible basis for its expansion. In the case of a "centered" stochastic process ("centered" means for all ) satisfying a technical continuity condition, admits a decomposition  where are pairwise uncorrelated random variables and the functions are continuous real-valued functions on that are pairwise orthogonal in . It is therefore sometimes said that the expansion is "bi-orthogonal" since the random coefficients are orthogonal in the probability space while the deterministic functions are orthogonal in the time domain. The general case of a process that is not centered can be brought back to the case of a centered process by considering which is a centered process. Moreover, if the process is Gaussian, then the random variables are Gaussian and stochastically independent. This result generalizes the "Karhunen–Loève transform". An important example of a centered real stochastic process on is the Wiener process; the Karhunen–Loève theorem can be used to provide a canonical orthogonal representation for it.