Abstract
Principal component analysis is a well developed and understood method of multivariate data processing. Its optimal performance requires knowledge of noise covariance that is not available in most applications. We suggest a method for estimation of noise covariance based on assumed smoothness of the estimated dynamics.
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CITATION STYLE
Šmídl, V., Kárný, M., Šámal, M., Backfrieder, W., & Szabo, Z. (2001). Smoothness prior information in principal component analysis of dynamic image data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2082, pp. 225–231). Springer Verlag. https://doi.org/10.1007/3-540-45729-1_24
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