Sequential principal component analysis

Charles Hsu; Harold Szu

doi:10.1117/12.887509

8 June 2011 Sequential principal component analysis

Charles Hsu, Harold Szu

Proceedings Volume 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX; 80580S (2011) https://doi.org/10.1117/12.887509
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

The Principal Component Analysis (PCA) is an optimal method for approximating a set of vectors or images, which was used in image processing and computer vision for a number of tasks including face and object recognition. The computational complexity and its batch calculation nature have limited its applications. Here we discuss the two different effective solutions to sequentially calculate the principal bases in terms of the eigenvectors with respective eigenvalues using the covariance (or covariance estimate), which is faster in typical applications and is especially advantageous for image sequences. This principal component basis calculation is processed with much lower delay and allows for dynamic updating of image databases.

Citation Download Citation

Charles Hsu and Harold Szu "Sequential principal component analysis", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80580S (8 June 2011); https://doi.org/10.1117/12.887509

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