The Karhunen-Loève theorem is a fundamental result in the field of stochastic processes and signal processing, providing a method for representing a stochastic process in terms of its orthogonal components. Specifically, it asserts that any square-integrable random process can be decomposed into a series of orthogonal functions, which can be expressed as a linear combination of random variables. This decomposition is particularly useful for dimensionality reduction, as it allows us to capture the essential features of the process while discarding noise and less significant information.
The theorem is often applied in areas such as data compression, image processing, and feature extraction. Mathematically, if is a stochastic process, the Karhunen-Loève expansion can be written as:
where are the eigenvalues, are uncorrelated random variables, and are the orthogonal functions derived from the covariance function of . This theorem not only highlights the importance of eigenvalues and eigenvectors in understanding random processes but also serves as a foundation for various applied techniques in modern data analysis.
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