Independent component analysis (ICA) using wavelet subband orthogonality

Harold H. Szu; Charles C. Hsu; Takeshi Yamakawa

doi:10.1117/12.304868

26 March 1998 Independent component analysis (ICA) using wavelet subband orthogonality

Harold H. Szu, Charles C. Hsu, Takeshi Yamakawa

Proceedings Volume 3391, Wavelet Applications V; (1998) https://doi.org/10.1117/12.304868
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

There are two kinds of RRP: (1) invertible ones, such as global Fourier transform (FT), local wavelet transform (WT), and adaptive wavelet transform (AWT); and (2) non-invertible ones, e.g. ICA including the global principle component analysis (PCA). The invertible FT and WT can be related to the non-invertible ICA when the continuous transforms are approximate din discrete matrix-vector operations. The landmark accomplishment of ICA is to obtain, by unsupervised learning algorithm, the edge-map as image feature ayields, shown by Helsinki researchers using fourth order statistics of nyields -- Kurosis K(uyields), and derived from information- theoretical first principle is augmented by the orthogonality property of the DWT subband used necessarily for usual image compression. If we take the advantage of the subband decorrelation, we have potentially an efficient utilization of a pari of communication channels if we could send several more mixed subband images through the pair of channels.

Citation Download Citation

Harold H. Szu, Charles C. Hsu, and Takeshi Yamakawa "Independent component analysis (ICA) using wavelet subband orthogonality", Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); https://doi.org/10.1117/12.304868

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